Method for enhancing an image derived from reflected ultrasound signals produced by an ultrasound transmitter and detector inserted in a bodily lumen

ABSTRACT

A device and method for intravascular ultrasound imaging. A catheter including ultrasonic apparatus is introduced into and may be moved through a bodily lumen. The apparatus transmits ultrasonic signals and detects reflected ultrasound signals which contain information relating to the bodily lumen. A processor coupled to the catheter is programmed to derive a first image or series of images and a second image or series of images from the detected ultrasound signals. The processor is also programmed to compare the second image or series of images to the first image or series of images respectively. The processor may be programmed to stabilize the second image in relation to the first image and to limit drift. The processor may also be programmed to monitor the first and second images for cardiovascular periodicity, image quality, temporal change and vasomotion. It can also match the first series of images and the second series of images.

FIELD OF THE INVENTION

The present invention relates to a device and method for enhanced imageand signal processing for Intravascular Ultrasound ("IVUS"), and morespecifically, to a device and method for processing IVUS image andsignal information which will enhance the quality and utility of IVUSimages.

BACKGROUND INFORMATION

IVUS images are derived from a beam of ultrasonic energy projected byapparatus such as a transducer or transducer array located around, alongor at the tip of a catheter inserted within a blood vessel. Anultrasound beam from the apparatus is continuously rotated within theblood vessel forming a 360° internal cross sectional image, i.e., theimage is formed in a transverse (X-Y) plane. Depending on the specificapparatus configuration, the image may be derived either from the sametransverse plane of the apparatus or from a transverse plane foundslightly forward (i.e., distal) of the transverse plane of theapparatus. If the catheter is moved inside and along the blood vessel(i.e., along the Z-axis), images of various segments (series ofconsecutive cross sections) of the vessel may be formed and displayed.

IVUS may be used in all types of blood vessels, including but notlimited to arteries, veins and other peripheral vessels, and in allparts of a body.

The ultrasonic signal that is received (detected) is originally ananalog signal. This signal is processed using analog and digital methodsso as to eventually form a set of vectors comprising digitized data.Each vector represents the ultrasonic response of a different angularsector of the vessel, i.e., a section of the blood vessel. The number ofdata elements in each vector (axial sampling resolution) and the numberof vectors used to scan a complete cross section (lateral samplingresolution) of the vessel may vary depending on the type of system used.

The digitized vectors may initially be placed into a two-dimensionalarray or matrix having Polar coordinates, i.e., A(r, θ). In this Polarmatrix, for example, the X axis corresponds to the r coordinate and theY axis corresponds to the θ coordinate. Each value of the matrix is avalue (ranging from 0-255 if the system is 8 bit) representing thestrength of the ultrasonic response at that location.

This Polar matrix is not usually transferred to a display because theresultant image will not be easily interpreted by a physician. Theinformation stored in the Polar matrix A(r, θ) usually undergoes severalprocessing stages and is interpolated into Cartesian coordinates, e.g.,X and Y coordinates (A(X, Y)) that are more easily interpreted by aphysician. Thus, the X and Y axis of matrix A(X, Y) will correspond tothe Cartesian representation of the vessel's cross-section. Theinformation in the Cartesian matrix possibly undergoes furtherprocessing and is eventually displayed for analysis by a physician.Images are acquired and displayed in a variable rate, depending on thesystem. Some systems can acquire and display images in video-displayrate, e.g., up to about 30 images per second.

IVUS examination of a segment of a bodily lumen, i.e., vessel isgenerally performed by situating the catheter distal (i.e., downstream)to the segment to be reviewed and then the catheter is pulled back(pullback) slowly along the bodily lumen (Z-axis) so that successiveimages that form the segment are continuously displayed. In many casesthe catheter is connected to a mechanical pulling device which pulls thecatheter at a constant speed (i.e., a typical speed is approximately0.5-1 mm/sec.).

In IVUS imaging systems today the technique described above fordisplaying an image of a cross section of a bodily lumen, e.g., bloodvessel, is generally used. These systems are deficient, however, becausethey do not include any form of stabilization of the images tocompensate for movements of the catheter and/or bodily lumen, e.g.,blood vessel. It is well known that during IVUS imaging of a bodilylumen, there is always motion exhibited by the catheter and/or thebodily lumen. This motion might be exhibited in the transverse (X-Y)plane, along the vessel axis (Z axis) or a combination of thosemovements. The imaging catheter can also be tilted in relation to thevessel so that the imaging plane is not perpendicular to the Z axis(This movement shall be termed as angulation). These movements arecaused by, among other things, beating of the heart, blood and/or otherfluid flow through the lumen, vasomotion, forces applied by thephysician, and other forces caused by the physiology of the patient.

In IVUS systems today, when the imaging catheter is stationary or whenperforming slow manual or mechanical pullback, relative movement betweenthe catheter and the lumen is the primary factor for the change inappearance between successive images, i.e., as seen on the displayand/or on film or video. This change in appearance occurs because therate of change of an image due to movements is much greater than therate of change in the real morphology due to pullback.

Stabilization occurs when the images include compensation for therelative movement between the catheter and the lumen in successiveimages. Because none of the IVUS systems used today performstabilization, there is no compensation for or correction of relativemovements between the catheter and the lumen. As a result, morphologicalfeatures are constantly moving or rotating, i.e., on the display and/orfilm or video. This makes it difficult for the physician to accuratelyinterpret morphology in an IVUS dynamic display. Furthermore, whennon-stabilized IVUS images are fed as an input to a processing algorithmsuch as 3D reconstruction or different types of filter that process aset of successive images, this can lead to degraded performance andmisdiagnosis or inaccurate determinations.

Current IVUS imaging apparatus or catheters may have occasionalmalfunctions of an electronic or mechanical origin. This can causedisplayed images to exhibit both recognized or unrecognized artifactsand obscure the real morphology. Currently there is no automatic methodsto determine whether images posses these types of artifacts which hamperthe analysis of the images of the vessel or bodily lumen.

The behavior of cardiovascular function is generally periodic. Thedetection of this periodicity and the ability to establish correlationbetween an image and the temporal phase in the cardiac cycle to which itbelongs is referred to as cardiac gating.

Currently, cardiac gating is performed by using an external signal,usually an ECG (Electro-Cardiogram). However, ECG gating requires boththe acquisition of the ECG signal and its interleaving (orsynchronization) with the IVUS image. This requires additionalhardware/software.

Morphological features in IVUS images of blood vessels can be brokeninto three general categories: the lumen, i.e., the area through whichthe blood or other bodily fluid flows; the vessel layers; and theexterior, i.e., the tissue or morphology outside of the vessel. Blood inmost IVUS films (images) is characterized by a rapidly changingspeckular pattern. The exterior of the vessel also alternates with hightemporal frequency. Currently, the temporal behavior of pixels and theirtextural attributes are not monitored automatically.

Vasomotion in the context of bodily lumens, e.g., blood vessel, isdefined as the change in the caliber of the lumen, e.g., vessel. Thischange can be brought about by natural circumstances or under inducedconditions. Vasomotion can have a dynamic component, i.e., dynamicchange of the lumen's dimensions, e.g., vessel's caliber (contractionand dilation) during the cardiovascular cycle, and a baseline staticcomponent, i.e., a change in the baseline caliber of the lumen, e.g.,vessel.

Vasomotion can be expressed as quantitative physiological parametersindicating the ability of the lumen, e.g., vessel to change its caliberunder certain conditions. These types of parameters have current andpossibly future medical and diagnostic importance in providinginformation regarding the state of the lumen, e.g., vessel and theeffect of the therapy performed.

IVUS can be used to monitor vasomotion because it provides an image ofthe lumen's baseline caliber and its dynamic changes. Additionally, IVUScan be used to monitor whether the vasomotion is global (uniform), i.e.,where the entire cross-section of the lumen contracts/dilates in thesame magnitude and direction. IVUS can also be used to determine whetherthe vasomotion is non-uniform which leads to local changes in thecaliber of the lumen, i.e., different parts of the lumen cross-sectionbehave differently.

Currently, all types of vasomotion monitoring by IVUS are performedmanually. This is tedious, time consuming, and prevents monitoring ofthe vasomotion in real time.

Interpretation of IVUS images is achieved through analysis of thecomposition of the static images and monitoring their temporal behavior.Most IVUS images can be divided into three basic parts. The most innersection is the flow passage of the lumen, i.e., the cavity through whichmatter, i.e., blood, flows. Around the flow passage is the actualvessel, which may include blood vessels and any other bodily vessels,which is composed of multiple layers of tissue (and plaque, ifdiseased). Outside the vessel other tissue which may belong to thesurrounding morphology, for example, the heart in a coronary vesselimage.

When the IVUS film is viewed dynamically, i.e., in film format, thepixels corresponding to matter flowing through the vessel and to themorphology exterior to the vessel exhibit a different temporal behaviorthan the vessel itself. For example, in most IVUS films, blood flowingthrough the vessel is characterized by a frequently alternating spekularpattern. The morphology exterior to the vessel also exhibits frequentalternation. Currently the temporal behavior of pixels in dynamic IVUSimages is not monitored automatically.

In current IVUS displays, if designed into the system, high frequencytemporal changes are suppressed by means such as averaging over a numberof images. However, this sometimes fails to suppress the appearance offeatures with high amplitudes, i.e., bright gray values, and it also hasa blurring effect.

The size of the flow passage of the lumen is a very important diagnosticparameter. When required for diagnosis, it is manually determined by,for example, a physician. This is accomplished by drawing the contour ofthe flow passage borders superimposed on a static image, e.g., frozen onvideo or on a machine display. This method of manual extraction is timeconsuming, inaccurate and subject to bias.

Currently, there is commercial image processing software for theautomatic extraction of the flow passage. However, these are based onthe gray value composition of static images and do not take into accountthe different temporal behavior exhibited by the material, e.g., bloodflowing through the passage as opposed to the vessel layers.

During treatment of vessels, it is common practice to repeat IVUSpullback examinations in the same vessel segments. For example, atypical situation is first to review the segment in question, evaluatethe disease (if any), remove the IVUS catheter, consider therapyoptions, perform therapy, e.g., PTCA-"balloon" or stenting, and thenimmediately thereafter reexamine the treated segment using IVUS in orderto assess the results of the therapy. To properly evaluate the resultsand fully appreciate the effect of the therapy performed, it isdesirable that the images of the pre-treated and post-treated segments,which reflect cross sections of the vessel lying at the same locationsalong the vessel's Z-axis (i.e., corresponding segments), be compared.To accomplish this comparison it must be determined which locations inthe films of the pre-treatment IVUS images and post-treatment IVUSimages correspond to one another. This procedure, called matching(registration) allows an accurate comparison of pre- and post-treatmentIVUS images.

Currently, matching is usually performed by viewing the IVUS pullbackfilms of pre- and post-treatment segments, one after the other or sideby side by using identifiable anatomical landmarks to locate thesequences that correspond visually to one another. This method isextremely imprecise and difficult to achieve considering that the imagesare unstable and often rotate and/or move around on the display due tothe absence of stabilization and because many of the anatomicallandmarks found in the IVUS pullback film of the pre-treatment segmentmay be disturbed or changed as a result of the therapy performed on thevessel. Furthermore, the orientation and appearance of the vessel islikely to change as a result of a different orientations and relativepositions of the IVUS catheter in relation to the vessel due to itsremoval and reinsertion after therapy is completed. The matching that isperformed is manual and relies primarily on manual visual identificationwhich can be extremely time consuming and inaccurate.

SUMMARY OF THE INVENTION

The present invention solves the problems associated with IVUS imagingsystems currently on the market and with the prior art by providingphysicians with accurate IVUS images and image sequences of themorphology being assessed, thereby enabling more accurate diagnosis andevaluation.

The present invention processes IVUS image and signal information toremove distortions and inaccuracies caused by various types of motion inboth the catheter and the bodily lumen. This results in both enhancedquality and utility of the IVUS images. An advantage provided by thepresent invention is that individual IVUS images are stabilized withrespect to prior image(s), thereby removing negative effects on anylater processing of multiple images. If the movements in each image areof the transverse type, then it is possible for the motion to becompletely compensated for in each acquired image.

The present invention also allows volume reconstruction algorithms toaccurately reproduce the morphology since movement of the bodily lumenis stabilized. The present invention is applicable to and useful in anytype of system where there is a need to stabilize images (IVUS or other)because a probe (e.g., ultrasonic or other) moving through a lumenexperiences relative motion (i.e., of the probe and/or of the lumen).

The present invention provides for detection of an ultrasonic signalemitted by ultrasonic apparatus in a bodily lumen, conversion of thereceived analog signal into Polar coordinates (A(r, θ)), stabilizationin the Polar field, converting the stabilized Polar coordinates intoCartesian coordinates (A(X, Y)), stabilization in the Cartesian fieldand then transferring the stabilized image as Cartesian coordinates to adisplay. Stabilized images, either in Polar or Cartesian coordinates,may be further processed prior to display or they might not bedisplayed. Conversion into Cartesian coordinates and/or stabilization inthe Cartesian field may be done at any point either before or afterstabilization in the Polar field. Additionally, either of Polar orCartesian stabilization may be omitted, depending on the detected shiftin the image and/or other factors. Furthermore, additional forms ofstabilization may be included or omitted depending on the detected shiftand/or other factors.

For example, stabilization of rigid motion may be introduced tocompensate for rotational motion (angular) or global vasomotion(expansion or contraction in the r direction) in the Polar field and/orfor Cartesian displacement (X and/or Y direction) in the Cartesianfield.

Transverse rigid motion between the representations of successive imagesis called a "shift," i.e., a uniform motion of all morphologicalfeatures in the plane of the image. To stabilize IVUS images, the firststep that is performed is "shift evaluation and detection." This iswhere the shift (if any) between each pair of successive images isevaluated and detected. The system may utilize a processor to perform anoperation on a pair of successive IVUS images to determine whether therehas been a shift between such images. The processor may utilize a singlealgorithm or may select from a number of algorithms to be used in makingthis determination.

The system utilizes the algorithm(s) to simulate a shift in an image andthen compares this shifted image to its predecessor image. Thecomparisons between images are known as closeness operations which mayalso be known in the prior art as matching. The system performs a singlecloseness operation for each shift. The results of the series ofcloseness operations is evaluated to determine the location (directionand magnitude) of the shifted image that bears the closest resemblanceto the predecessor unshifted image. An image can of course be comparedin the same manner to its successor image. After the actual shift isdetermined, the current image becomes the predecessor image, the nextimage becomes the current image and the above operation is repeated.

Using shift evaluation and detection, the system determines the type oftransverse shift, e.g., rotational, expansion, contraction, displacement(Cartesian), etc., along with the direction and magnitude of the shift.The next step is "shift implementation." This is where the systemperforms an operation or a series of operations on successive IVUSimages to stabilize each of the images with respect to its adjacentpredecessor image. This stabilization utilizes one or multiple "reverseshifts" which are aimed at canceling the detected shift. The system mayinclude an algorithm or may select from a number of algorithms to beused to implement each "reverse shift." The logic which decides uponwhat reverse shift will actually be implemented on an image, prior toits feeding to further processing or display, is referred to as "shiftlogic". Once the IVUS images are stabilized for the desired types ofdetected motion, the system may then transfer the Cartesian (or Polar)image information for further processing and finally for display wherethe results of stabilization may be viewed, for example, by a physician.Alternatively, stabilization can be invisible to the user in the sensethat stabilization can be used prior to some other processing steps,after which, resulted images are projected to the display in theiroriginal non-stabilized posture or orientation.

It is possible that the transverse motion between images will not berigid but rather of a local nature, i.e., different portions of theimage will exhibit motion in different directions and magnitudes. Inthat case the stabilization methods described above or other types ofmethods can be implemented on a local basis to compensate for suchmotion.

The present invention provides for detection of the cardiac periodicityby using the information derived only from IVUS images without the needfor an external signal such as the ECG. This process involves closenessoperations which are also partly used in the stabilization process. Oneimportant function of detecting periodicity (i.e., cardiac gating), whenthe catheter is stationary or when performing controlled IVUS pullback,is that it allows the selection of images belonging to the same phase insuccessive cardiac cycles. Selecting images based on the cardiac gatingwill allow stabilization of all types of periodic motion (includingtransverse, Z-axis and angulations) in the sense that images areselected from the same phase in successive heart-beats. These IVUSimages, for example, can be displayed and any gaps created between themmay be compensated for by filling in and displaying interpolated images.The IVUS images selected by this operation can also be sent onward forfurther processing.

The closeness operations used for periodicity detection can also beutilized for monitoring image quality and indicate artifacts associatedwith malfunction of the imaging and processing apparatus.

Operations used for shift evaluation can automatically indicatevasomotion. This can serve the stabilization process as vasomotioncauses successive images to differ because of change in the vessel'scaliber. If images are stabilized for vasomotion, then this change iscompensated for. Alternatively, the information regarding the change incaliber may be displayed since it might have physiological significance.Monitoring of vasomotion is accomplished by applying closenessoperations to successive images using their Polar representations, i.e.,A(r, θ). These operations can be applied between whole images or betweencorresponding individual Polar vectors (from successive images),depending on the type of information desired. Since global vasomotion isexpressed as a uniform change in the lumen's caliber it can be assessedby a closeness operation which takes into account the whole Polar image.In general, any operation suitable for global stabilization in the Polarrepresentation can be used to assess global vasomotion.

Under certain conditions during IVUS imaging there may be non-uniformvasomotion, i.e., movement only in certain sections of the IVUS imagecorresponding to specific locations in the bodily lumen. This may occur,for example, where an artery has a buildup of plaque in a certainlocation, thereby allowing expansion or contraction of the artery onlyin areas free of the plaque buildup. When such movement is detected thesystem is able to divide the ultrasound signals representing crosssections of the bodily lumen into multiple segments which are then eachprocessed individually with respect to a corresponding segment in theadjacent image using certain algorithm(s). The resulting IVUS images maythen be displayed. This form of stabilization may be used individuallyor in conjunction with the previously discussed stabilizationtechniques. Alternatively, the information regarding the local change invessel caliber can be displayed since it might have physiologicalsignificance.

The temporal behavior of pixels and their textural attributes couldserve for: enhancement of display; and automatic segmentation (lumenextraction). If monitored in a stabilized image environment then theperformance of the display enhancement and segmentation processes may beimproved.

According to the present invention, the temporal behavior of IVUS imagesmay be automatically monitored. The information extracted by suchmonitoring can be used to improve the accuracy of IVUS imageinterpretation. By filtering and suppressing the fast changing featuressuch as the matter, e.g., blood flowing through the vessel and themorphology exterior to the vessel as a result of their temporalbehavior, human perception of the vessel on both static images anddynamic images, e.g., images played in cine form, may be enhanced.

Automatic segmentation, i.e., identification of the vessel and thematter, e.g., blood flowing through the vessel may be performed by usingan algorithm which automatically identifies the matter, e.g., bloodbased on the temporal behavior of textural attributes formed by itscomprising pixels. The temporal behavior that is extracted from theimages can be used for several purposes. For example, temporal filteringmay be performed for image enhancement, and detection of the changes inpixel texture may be used for automatic identification of the lumen andits circumference.

In all IVUS images, the catheter itself (and imaging apparatus) is bestto be eliminated from the image prior to performing stabilization or formonitoring. Failure to eliminate the catheter might impair stabilizationtechniques and monitoring. Elimination of the catheter may be performedautomatically since its dimensions are known.

The present invention also provides for automatic identification (i.e.,matching or registration) of corresponding frames of two different IVUSpullback films of the same segment of a vessel, e.g., pre-treatment andpost-treatment. To compare a first IVUS pullback film, i.e., a firstIVUS imaging sequence, with a second IVUS pullback film, i.e., a secondIVUS imaging sequence, of the same segment of a bodily lumen, forexample, captured on video, film or in digitized form, the imagingsequences must be synchronized. Matching, which will achieve thissynchronization, involves performing closeness operations between groupsof consecutive images belonging to the two sets of IVUS imagingsequences.

Out of one imaging sequence a group of consecutive images, termed thereference group, is selected. This group should be selected from aportion of the vessel displayed in both imaging sequences and it shouldbe a portion on which therapy will not be performed since the morphologyof the vessel is likely to change due to therapy. Another condition forthis matching process is that the two imaging sequences are acquired ata known, constant and preferably the same pullback rate.

Closeness operations are performed between the images of the referencegroup and the images from the second group which has the same number ofsuccessive images extracted from the second imaging sequence. Thissecond group of images is then shifted by a single frame with respect tothe reference group and the closeness operations are repeated. This maybe repeated for a predetermined number of times and the closenessresults of each frame shift are compared to determine maximal closeness.Maximal closeness will determine the frame displacement between theimages of the two imaging sequences. This displacement can be reversedin the first or second film so that corresponding images may beautomatically identified and/or viewed simultaneously.

Thus, corresponding images may be viewed, for example, to determine theeffectiveness of any therapy performed or a change in the morphologyover time. Additionally, the various types of stabilization discussedabove may be implemented within or between the images in the twosequences, either before, during or after this matching operation. Thus,the two films can be displayed not only in a synchronized fashion, butalso in the same orientation and posture with respect to one another.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1(a) and (b) show a two-dimensional array or matrix of an imagearranged in digitized vectors in Polar and Cartesian coordinates,respectively.

FIG. 2 illustrates the results of a shift evaluation between twosuccessive images in Cartesian coordinates.

FIG. 3 shows images illustrating the occurrence of drift phenomena inPolar and Cartesian coordinates.

FIG. 4 illustrates the effect of performing stabilization operations(rotational and Cartesian shifts) on an image.

FIG. 5 illustrates global contraction or dilation of a bodily lumenexpressed in the Polar representation of the image and in the Cartesianrepresentation of the image.

FIG. 6 shows an image divided into four sections for processingaccording to the present invention.

FIG. 7 shows a vessel, in both Cartesian and Polar coordinates, in whichlocal vasomotion has been detected.

FIG. 8 illustrates the results of local vasomotion monitoring in a realcoronary vessel in graphical form.

FIG. 9 shows an ECG and cross-correlation coefficient plottedgraphically in synchronous fashion.

FIG. 10 shows a table of a group of cross-correlation coefficient values(middle row) belonging to successive images (numbers 1 through 10 shownin the top row) and the results of internal cross-correlations (bottomrow).

FIG. 11 shows a plot of a cross-correlation coefficient indicating anartifact in IVUS images.

FIG. 12 shows an IVUS images divided into three basic parts: the lumenthrough which fluid flows; the actual vessel; and the surroundingtissue.

FIG. 13 illustrates the results of temporal filtering.

FIG. 14 shows an image of the results of the algorithm for automaticextraction of the lumen.

FIG. 15 illustrates the time sequence of a first film (left column),reference segment from the second film (middle column) and the imagesfrom the first film which correspond (or match) the images of thereference segment (right column).

DETAILED DESCRIPTION

In intravascular ultrasound (IVUS) imaging systems the ultrasonicsignals are emitted and received by the ultrasonic apparatus, forexample, a transducer or transducer array, processed and eventuallyarranged as vectors comprising digitized data. Each vector representsthe ultrasonic response of a different angular sector of the bodilylumen. The number of data elements in each vector (axial samplingresolution) and the number of vectors used to scan the completecross-section (lateral sampling resolution) of the bodily lumen dependson the specific IVUS system used.

The digitized vectors are initially packed into a two-dimensional arrayor matrix which is illustrated in FIG. 1(a). Generally, this matrix haswhat are known as Polar coordinates, i.e., coordinates A(r, θ). TheX-axis of the matrix shown in FIG. 1(a) corresponds to the r coordinatewhile the Y-axis of the matrix corresponds to the θ coordinate. Eachvalue of the matrix is generally a gray value, for example, ranging from0-255 if it is 8 bit, representing the strength of the ultrasonic signalat that corresponding location in the bodily lumen. This Polar matrixmay then be converted into a Cartesian matrix as shown in FIG. 1(b)having an X-axis and Y-axis which correspond to the Cartesianrepresentation of the vessel's cross-section. This image may then befurther processed and transferred to a display. The initial array andthe display may each utilize either Polar or Cartesian coordinates. Thevalues for the matrix may be other than gray values, for example, theymay be color values or other values and may be less than or more than 8bits.

During an IVUS imaging pullback procedure the bodily lumen, hereinafterreferred to as a vessel, and/or the imaging catheter may experienceseveral modes of relative motion. These types of motion include: (1)Rotation in the plane of the image, i.e., a shift in the θ-coordinate ofthe Polar image; (2) Cartesian displacement, i.e., a shift in the Xand/or Y coordinate in the Cartesian image; (3) Global vasomotion,characterized by a radial contraction and expansion of the entirevessel, i.e., a uniform shift in the r-coordinate of the Polar image;(4) Local vasomotion, characterized by a radial contraction andexpansion of different parts of the vessel with different magnitudes anddirections, i.e., local shifts in the r-coordinate of the Polar image;(5) Local motion, characterized by different tissue motion which varydepending on the exact location within the image; and (6) Through planemotion, i.e., movements which are perpendicular or near perpendicular(angulation) to the plane of the image.

Stabilization of successive raw images is applicable to the first 5types of motion described above because motion is confined to thetransverse plane. These types of motion can be compensated for, andstabilization achieved, by transforming each current image so that itsresemblance to its predecessor image is maximized. The first 3 types ofmotion can be stabilized using closeness operations which compare wholeor large parts of the images one to another. This is because the motionis global or rigid in its nature. The 4th and 5th types of motion arestabilized by applying closeness operations on a localized basis becausedifferent parts of the image exhibit different motion. The 6th type ofmotion can be only partly stabilized by applying closeness operations ona localized basis. This is because the motion is not confined to thetransverse plane. This type of motion can be stabilized usingcardiovascular periodicity detection.

The next sections shall describe methods for global stabilization,followed by a description of methods for local stabilization.Stabilization using cardiovascular periodicity detection shall bedescribed in the sections discussing periodicity detection.

To achieve global stabilization, shift evaluation is performed usingsome type of closeness operation. The closeness operation measures thesimilarity between two images. Shift evaluation is accomplished bytransforming a first image and measuring its closeness, i.e.,similarity, to its predecessor second image. The transformation may beaccomplished, for example, by shifting the entire first image along anaxis or a combination of axes (X and/or Y in Cartesian coordinates or rand/or θ in Polar coordinates) by a single pixel (or more). Once thetransformation, i.e., shift is completed the transformed first image iscompared to the predecessor second image using a predefined function.This transformation is repeated, each time by shifting the first imagean additional pixel (or more) along the same and/or other axis andcomparing the transformed first image to the predecessor second imageusing a predefined function. After all of the shifts are evaluated, thelocation of the global extremum of the comparisons using the predefinedfunction will indicate the direction and magnitude of the movementbetween the first image and its predecessor second image.

For example, FIG. 2 illustrates the results of a shift evaluationbetween two successive images in Cartesian coordinates. Image A is apredecessor image showing a pattern, e.g., a cross-section of a vessel,whose center is situated in the bottom right quadrant of the matrix.Image B is a current image showing the same pattern but moved in anupward and left direction and situated in the upper left quadrant of thematrix. The magnitude and direction of the movement of the vessel'scenter is indicated by the arrow. The bottom matrix is the C(shiftX,shiftY) matrix which is the resulting matrix after performing shiftevaluations using some type of closeness operation.

There are many different algorithms or mathematical functions that canbe used to perform the closeness operations. One of these iscross-correlation, possibly using Fourier transform. This is where thecurrent and predecessor images each consisting of, for example, 256×256pixels, are each Fourier transformed using the FFT algorithm. Theconjugate of the FFT of the current image is multiplied with the FFT ofthe predecessor image. The result is inversely Fourier transformed usingthe IFFT algorithm. The formula for cross-correlation using Fouriertransform can be shown as follows:

    C=real(ifft2((fft2(A))*conj(fft2(B))))

where:

A=predecessor image matrix (e.g., 256×256);

B=current image matrix (e.g., 256×256);

fft2=two dimensional FFT;

ifft2=two dimensional inverse FFT;

conj=conjugate;

real=the real part of the complex expression;

*=multiplication of element by element; and

C=cross-correlation matrix.

Evaluating closeness using cross-correlation implemented by Fouriertransform is actually an approximation. This is because the mathematicalformula for the Fourier transform relates to infinite or periodicfunctions or matrices, while in real life the matrices (or images) areof a finite size and not necessarily periodic. When implementingcross-correlation using FFT, the method assumes periodicity in bothaxes.

As a result, this formula is a good approximation and it reflects theactual situation in the θ-axis of the Polar representation of the image,however, it does not reflect the actual situation in the r-axis of thePolar representation or of the X- or Y-axis of the Cartesianrepresentation of the image.

There are a number of advantages to cross-correlation utilizing FFT.First, all values of the cross-correlation matrix C(shiftX, shiftY) arecalculated by this basic operation. Furthermore, there is dedicatedhardware for the efficient implementation of the FFT operation, i.e.Fourier transform chips or DSP boards.

Another algorithm that can be used to perform closeness operations isdirect cross-correlation, either normalized or not. This is achieved bymultiplying each pixel in the current shifted image by its correspondingpixel in the predecessor image and summing up all of the results andnormalizing in the case of normalized cross-correlation. Each shiftresults in a sum and the actual shift will be indicated by the largestsum out of the evaluated shifts. The formula for cross-correlation canbe shown by the following formula: ##EQU1##

The formula for normalized cross correlation is ##EQU2## where:A=predecessor image matrix;

B=current image matrix;

*=multiplication of pixel by corresponding pixel;

Σ=sum of all pixels in matrix;

C=matrix holding results for all performed shifts.

Using this direct method of cross-correlation, C(shiftX, shiftY) can beevaluated for all possible values of shiftX and shiftY. For example, ifthe original matrices, A and B, have 256×256 pixels each, then shiftXand shiftY values, each ranging from -127 to +128 would have to beevaluated, making a total of 256×256=65,536 shift evaluations in orderfor C(shiftX, shiftY) to be calculated for all possible values of shiftXand shiftY. Upon completion of these evaluations the global maximum ofthe matrix is determined.

Direct cross-correlation can be implemented more efficiently by loweringthe number of required arithmetic operations. In order to detect theactual shift between images, evaluation of every possible shiftX andshiftY is not necessary. It is sufficient to find the location of thelargest C(shiftX, shiftY) of all possible shiftX and shiftY.

A third algorithm that can be used to perform closeness operations isthe sum of absolute differences (SAD). This is achieved by subtractingeach pixel in one image from its corresponding pixel in the other image,taking their absolute values and summing up all of the results. Eachshift will result in a sum and the actual shift will be indicated by thelowest sum. The formula for sum of absolute differences (SAD) can beshown as follows:

    SAD=absolute(A-B)

This formula can also be shown as follows: ##EQU3## where: A=predecessorimage matrix;

B=current image matrix;

abs=absolute value.

-=subtraction of element by element; and

Σ=sum of all differences.

While the accuracy of each of these algorithms/formulas may varyslightly depending on the specific type of motion encountered and systemsettings, it is to be understood that no single formula can, a-priori beclassified as providing the best or most accurate results. Additionally,there are numerous variations on the formulas described above and otheralgorithms/formulas that may be utilized for performing shift evaluationand which may be substituted for the algorithms/formulas describedabove. These algorithms/formulas also include those operations known inthe prior art for use as matching operations

Referring again to FIG. 2, if the closeness operation performed iscross-correlation, then C(shiftX, shiftY) is called thecross-correlation matrix and its global maximum (indicated by the blackdot in the upper left quadrant) will be located at a distance anddirection from the center of the matrix (arrow in matrix C) which is thesame as that of the center of the vessel in Image B relative to thecenter of the vessel in image A (arrow in Image B).

If the closeness operation performed is SAD, then the black dot wouldindicate the global minimum which will be located at a distance anddirection from the center of the matrix (arrow in matrix C) which is thesame as that of the center of the vessel in Image B relative to thecenter of the vessel in Image A (arrow in Image B).

Rotational motion is expressed as a shift along the current Polar imagein the θ-coordinate relative to its predecessor. The rotational shift ina current image is detected by maximizing the closeness between thecurrent Polar image and its predecessor. Maximum closeness will beobtained when the current image is reversibly shifted by the exactmagnitude of the actual shift. In for example, a 256×256 pixel image,the value of the difference (in pixels) between 128 and the θ-coordinateof the maximum in the cross-correlation image (minimum in the SADimage), will indicate the direction (positive or negative) and themagnitude of the rotation.

Global vasomotion is characterized by expansion and contraction of theentire cross section of the vessel. In the Polar image this type ofmotion is expressed as movement inwards and outwards of the vessel alongthe r-axis. Vasomotion can be compensated by performing the oppositevasomotion action on a current Polar image in relation to itspredecessor Polar image using one of the formulas discussed above orsome other formula. In contrast to angular stabilization, vasomotionstabilization does not change the orientation of the image but actuallytransforms the image by stretching or compressing it.

Cartesian displacement is expressed as a shift in the X-axis and/orY-axis in the Cartesian image relative to its predecessor. This type ofmotion is eliminated by shifting the Cartesian image in an oppositedirection to the actual shift. Thus, Cartesian displacement, in theCartesian representation, can be achieved by essentially the samearithmetic operations used for rotational and vasomotion stabilizationin the Polar representation.

The number of shift evaluations necessary to locate the global extremum(maximum or minimum, depending on the closeness function) of C(shiftX,shiftY) may be reduced using various computational techniques. Onetechnique, for example, takes advantage of the fact that motion betweensuccessive IVUS images is, in general, relatively low in relation to thefull dimensions of the Polar and/or Cartesian matrices. This means thatC(shiftX, shiftY) can be evaluated only in a relatively small portionaround the center of the matrix, i.e., around shiftX=0, shiftY=0. Theextremum of that portion is assured to be the global extremum of matrixC(shiftX, shiftY) including for larger values of shiftX and shiftY. Thesize of the minimal portion which will assure that the extremum detectedwithin it is indeed a global extremum varies depending on the systemsettings. The number of necessary evaluation operations may be furtherreduced by relying on the smoothness and monotonous property expectedfrom the C matrix (especially in the neighborhood of the globalextremum). Therefore, if the value in the C(shiftX, shiftY) matrix at acertain location is a local extremum (e.g, in a 5×5 pixel neighborhood),then it is probably the global extremum of all of matrix C(shiftX,shiftY).

Implementing this reduction of the number of necessary evaluations canbe accomplished by first searching from the center of the matrix(shiftX=0, shiftY=0) and checking a small neighborhood, e.g., 5×5 pixelsaround the center. If the local extremum is found inside thisneighborhood then it is probably the global extremum of the whole matrixC(shiftX, shiftY) and the search may be terminated. If, however, thelocal extremum is found on the edges of this neighborhood, e.g.,shiftX=-2, shiftX=2, shiftY=-2 or shiftY=2, then the search is repeatedaround this pixel until a C(shiftX, shiftY) value is found that isbigger (smaller) than all of its close neighbors. Because in a largenumber of images there is no inter-image motion, the number ofevaluations needed to locate the global extremum in those cases, will beapproximately 5×5=25, instead of the original 65,536 evaluations.

The number of necessary evaluation operations may also be reduced bysampling the images. For example, if 256×256 sized images are sampledfor every second pixel then they are reduced to 128×128 sized matrixes.In this case, direct cross-correlation or SAD, between such matrixesinvolve 128×128 operations instead of 256×256 operations, each time theimages are shifted one in relation to the other. Sampling, as areduction method for shift evaluation operations can be interleaved withother above described methods for reduction.

Referring again to FIG. 2, as a result of the closeness operation, theindicated shiftX will have a positive value and shiftY a negative value.In order to stabilize Image B, i.e., compensate for the shifts in the Xand Y directions, shift logic will reverse the shifts, i.e., changetheir sign but not their magnitude, and implement these shifts on thematrix corresponding to Image B. This will artificially reverse theshift in Image B and cause Image B to be unshifted with respect to ImageA.

The actual values used in the closeness calculations need notnecessarily be the original values of the matrix as supplied by theimaging system. For example, improved results may be achieved when theoriginal values are raised to the power of 2, 3 or 4 or processed bysome other method.

The imaging catheter and the enclosing sheath appear as constantartifacts in all IVUS images. This feature obscures closeness operationsperformed between images since it is not part of the morphology of thevessel. It is, therefore, necessary to eliminate the catheter andassociated objects from each image prior to performing closenessoperations, i.e., its pixels are assigned a value of zero. Theelimination of these objects from the image may be performedautomatically since the catheter's dimensions are known.

Shift evaluation and implementation may be modular. Thus, shiftevaluation and implementation may be limited to either Polar coordinatesor Cartesian coordinates individually, or shift evaluation andimplementation may be implemented sequentially for Polar and Cartesiancoordinates. Presently, because imaging in IVUS systems is generallyorganized by first utilizing Polar coordinates and then converting intoCartesian coordinates, it is most convenient to perform shift evaluationand implementation in the same sequence. However, the sequence may bemodified or changed without any negative effects or results.

The shift evaluation process can be performed along one or two axis. Ingeneral, two dimensional shift evaluation is preferred even when motionis directed along one axis. Shift implementation may be limited to bothaxis, one axis or neither axis.

There is not a necessary identity between the area in the image used forshift evaluation and between the area on which shift implementation isperformed. For example, shift evaluation may be performed using arelatively small area in the image while shift implementation will shiftthe whole image according to the shift indicated by this area.

A trivial shift logic is one in which the shift implemented on eachimage (thereby forming a stabilized image) has a magnitude equal, and inopposite direction, to the evaluated shift. However, such logic canresult in a process defined as Drift. Drift is a process in whichimplemented shifts accumulate and produce a growing shift whosedimensions are significant in relation to the entire image or display.Drift may be a result of inaccurate shift evaluation or non-transverseinter-image motion at some part of the cardiovascular cycle. WhenCartesian stabilization is implemented, drift can cause, for example,the shifting of a relatively large part of the image out of the display.When rotational stabilization is implemented, drift can cause theincreasing rotation of the image in a certain direction.

FIG. 3 is an image illustrating the occurrence of drift in Polar andCartesian coordinates. The left image is the original display of theimage while the right image is the same image after Polar and Cartesianstabilization has been performed. Note how the right image is rotatedcounter-clockwise in a large angle and shifted downward in relation tothe left image. In this case, rotational and Cartesian shiftimplementation do not compensate for actual shifts in the image, butrather arise from inaccurate shift evaluation.

The shift logic must be able to deal with this drift so that there willbe a minimal implementation of mistaken evaluated shifts. One method forpreventing, or at least limiting drift is by setting a limit to themagnitude of allowable shifts. This will minimize the drift but at thecost of not compensating for some actual shift. Additional methods canbe used to prevent or minimize shift. These may possibly be interleavedwith cardiovascular periodicity detection methods discussed later.

The images shown in FIG. 4 illustrate the effect of performingstabilization operations (rotational and Cartesian shifts) on an image.The left image is an IVUS image from a coronary artery as it would lookon a large portion of a regular display (with catheter deleted) whilethe right image shows how the left image would be displayed afterstabilization operations are implemented.

Taking a close look at the left and right images in FIG. 4, certaindifferences can be observed. First, the right image is slightly rotatedin a clockwise direction (i.e., by a few degrees) in relation to theleft image. This is the result of rotational stabilization.

Next, the right image is translated in a general left direction inrelation to the left image. This can be detected by noting the distanceof the lumen (cavity) from the edges of the picture in each image. Thisis a result of Cartesian shift stabilization operations.

The advantages of stabilization of the displayed image cannot beappreciated by viewing single images as shown in FIG. 4. However,viewing a film of such images would readily illustrate the advantages.In a display which does not include stabilization, the location of thecatheter would always be situated in the center of the display and themorphological features would move around and rotate on the display. Incontrast, in a stabilized display, the location of the catheter wouldmove around while the morphological features would remain basicallystationary. Stabilization does not necessarily have to be exhibited onan actual display. It can be invisible to the user in the sense thatstabilization will enhance subsequent processing steps, but the actualdisplay will exhibit the resultant processed images in their original(non-stabilized) posture and orientation.

FIG. 5 illustrates global contraction or dilation of a vessel, expressedin the Polar representation of the image as a movement of the featuresalong the r-coordinates, i.e., movement along the Polar vectors. FIG. 5also shows the same global contraction or dilation expressed in theCartesian representation of the image. FIG. 5(a) shows the baselineappearance of the cross section of a vessel in both the Polar andCartesian representations. FIG. 5(b) shows a relative to baselinecontraction of the vessel. FIG. 5(c) shows a relative to baselineuniform dilation of the vessel.

Since global vasomotion is expressed as a uniform change in the vessel'scaliber, any operation suitable for stabilization in the Polarrepresentation can be used to assess global vasomotion, e.g., it can beassessed by a closeness operation utilizing the entire Polar image.

After two dimensional shift evaluation is performed, as discussed above,the location of the maximum in matrix C(shiftX, shiftY) on the θ-axis isutilized for rotational stabilization. This leaves the location of theextremum on the r-axis, which can be used as an indication of globalvasomotion. Thus, global vasomotion monitoring is a by-product of twodimensional shift evaluation in the Polar image.

Each pair of successive images produce a value indicative of thevasomotion. Both the magnitude and the sign of the resulting shiftbetween images characterize the change in the vessel, i.e., vasomotion.Negative shifts indicate dilation, and positive shifts indicatecontraction. The magnitude of the value indicates the magnitude of thevasomotion change.

Under certain circumstances motion or vasomotion may not beuniform/rigid although confined to the plane of the image, i. e.,transverse. To determine the type of motion or vasomotion, the image maybe divided into sections and global stabilization evaluation performedon each of these sections. By examining the indicated shifts of thesesections relative to the corresponding sections in the predecessorimage, a determination can be made as to the type of motion. Forexample, as shown in FIG. 6, the image in FIG. 6(a) can be divided intofour sections as shown in FIG. 6(b). Shift evaluation can be performedseparately on each of the four sections. Comparison between the resultsof the shift evaluation for each of the four sections can possiblyidentify the type of actual motion. Thus, the type of stabilizationapplied can be varied depending on the type of motion detected.

Stabilization for local motion is achieved by performing closenessoperations on a localized basis. Small portions of the predecessor imageA ("template" regions) and small portions of the current image B("search" regions) participate in the local stabilization process.Sometimes, it is best to perform local stabilization after globalstabilization has been performed.

During local stabilization, template regions in the predecessor image(A) are shifted within search regions and compared, using closenessoperations to template sized regions in the current image (B). Eachpixel, in the (newly) formed stabilized image (B') will be assigned anew value based on the results of the search and closeness evaluationperformed.

Local stabilization is illustrated by the following example in which thetemplate region is a 1×1 pixel region, i.e., a single pixel, the searchregion is a 3×3 pixel region and the closeness operation is SAD. In thefollowing diagram, the pixel valued 3 in A and the pixel valued 9 in Bare corresponding pixels. The 3×3 pixel neighborhood of the pixel valued9 is also illustrated.

    ______________________________________                                        Pixel in A ("template" region)                                                               Pixels in B (3 × 3 "search" region)                                                        B`                                          ______________________________________                                                       1  10 10                                                       3              7   9 50           1                                                          11  7 60                                                       ______________________________________                                    

In this example, according to the conditions described above the`template` pixel valued 3 is compared using SAD to all pixels found inthe 3×3 search region around the pixel valued 9. The pixel valued 1 atthe top left corner of the search region will achieve the minimal SADvalue (|1-3|=2) out of all the possibilities in the search region. As aresult, in the newly formed stabilized image (B'), the pixelcorresponding in location to pixels valued 3 and 9 will be assigned thevalue of 1.

In general, the dimensions of the template and search region can bevaried along with the closeness operations used. The actual value whichis assigned to the pixel of the newly formed stabilized image (B') neednot necessarily be an actual pixel value from the current image B (asillustrated in the example) but some function of pixel values. It isimportant to note that as a result of local stabilization, as opposed tothe global/rigid methods, the "composition" of the image, i.e., theinternal relationship between pixels, and their distribution in thestabilized image, changes in relation to the original image. Localstabilization can be implemented on both the Polar and Cartesianrepresentations of the image.

FIG. 7 shows a vessel, in both Cartesian and Polar coordinates, in whichlocal vasomotion has been detected. When local vasomotion is detected,it is an indication that some parts of the cross-section of the vesselare behaving differently than other parts of the cross-section.

FIG. 7(a) shows a baseline figure of the vessel prior to localvasomotion. FIG. 7(b) shows an example of local vasomotion. As indicatedin both the Cartesian and Polar representations, four distinct parts ofthe vessel behave differently: two segments of the vessel do not changecaliber, or do not move relative to their corresponding segments in thepredecessor image; one segment contracts, or moves up; and one segmentdilates, or moves down.

As can be observed, global vasomotion evaluation methods are notappropriate for evaluating local vasomotion because the vessel does notbehave in a uniform manner. If global vasomotion evaluation was to beapplied, for example, on the example shown in FIG. 7, it might detectoverall zero vasomotion, i.e. the contraction and dilation would canceleach other.

Therefore, local vasomotion evaluation methods must be utilized. Thismay be achieved by separately evaluating vasomotion in each Polarvector, i.e., in each θ (or Y) vector. Closeness operations are appliedusing one dimensional shifts in corresponding Polar vectors. Forexample, if closeness is utilized with cross-correlation, then thefollowing operation illustrates how this is accomplished using onedimensional shifts. ##EQU4## where: A=predecessor image matrix;

B=current image matrix;

*=multiplication of pixel by corresponding pixel;

Σ=sum of pixels in the matrix of the Polar vector;

C=two dimensional matrix of correlation coefficient.

As can be seen, shifting is performed along one axis (X or r-axis) foreach and every Polar vector (θ or Y vector). The values assigned in eachvector for shift evaluation may not be the actual values of the imagesbut, for example, each pixel in the vector can be assigned the averageof its lateral neighbors, i.e., A(X, Y) will be assigned, for example,the average of A(X, Y-1), A(X, Y) and A(X, Y+1). The same goes forB(shiftX, Y). This can make the cross-correlation process more robust tonoise.

A two dimensional matrix (C(shiftX, Y)) is formed. Each column in thematrix stores the results of closeness/similarity operations performedbetween corresponding Polar vectors from the current image and thepredecessor image. This operation could also have been implemented usingFFT.

After formation of the matrix, the location of the extremum (maximum inthe cross-correlation operation) in each column is detected. Thisextremum location indicates the match between the current Polar vectorand its predecessor. Thus, the vasomotion in each vector can becharacterized, i. e., the radial movement in each specific angularsector of the vessel.

This information can be used to display the local vasomotion, it can beadded up from some or all Polar vectors and averaged to determine anaverage value for the vasomotion, or it can be used for other purposes.Therefore, by evaluating local vasomotion, both local and globalvasomotion can be evaluated.

To be effectively used and/or expressed as quantitative physiologicalparameters, the magnitude of vasomotion must relate in some fashion tothe vessel's actual caliber. Thus, measurements of vasomotion monitoringshould generally be used in conjunction with automatic or manualmeasurements of the vessel's caliber

Besides for true vasomotion, Cartesian displacement may also be detectedas vasomotion. This is because Cartesian displacement, when expressed inPolar coordinates, results in shifts along both the r and θ axes. Todistinguish true vasomotion from Cartesian displacement, shiftevaluation in the Cartesian image must indicate no, or little motion. IfCartesian displacement is detected, then it must first be stabilized.Thereafter, the Cartesian coordinates may be converted back into Polarcoordinates for vasomotion evaluation. This will allow greater successand provide more accurate results when determining actual vasomotion.

The graphs in FIG. 8 illustrate the results of local vasomotionmonitoring in a human coronary vessel in vivo. Local vasomotionmonitoring was performed twice in approximately the same segment of thevessel, and consisted of 190 successive images as shown (X-axis) inFIGS. 8(a) and 8(b). The difference between the two graphs is that thevasomotion evaluation shown in FIG. 8(a) was performed prior totreatment of the artery, i.e., pre-intervention, while the vasomotionevaluation shown in FIG. 8(b) was performed after treatment of theartery, i.e., post-intervention.

In every image, vasomotion was assessed locally in every Polar vectorand then all detected individual shifts were added and averaged toproduce a single global vasomotion indication (Y-axis) for each image,i.e., an indication for vasomotion activity.

The units on the Y-axis do not have a direct physiological meaningbecause the actual caliber of the vessel was not calculated, but therelationship between the values in FIGS. 8(a) and 8(b) have a meaningbecause they were extracted from the same vessel. Thus, importantinformation may be derived from these figures. Note how the vasomotionincreased after treatment (maximal vasomotion from approximately 40 toapproximately 150). Therefore, even though vasomotion was not fullyquantified, a change in physiology (probably linked to the treatment)has been demonstrated.

Cardiovascular periodicity may be monitored solely based on informationstored in IVUS images, thereby eliminating the need for an ECG or anyother external signal. This means that a link can be established betweenevery image and its respective temporal phase in the cardiovascularcycle without need for an external signal. Once this linkage isestablished, then monitoring can substitute the ECG signal in a largenumber of utilities which require cardiac gating. This monitoring may beaccomplished using closeness operations between successive images.Moreover, the same closeness operations can produce informationregarding the quality of IVUS images and their behavior.

The cardiac cycle manifests itself in the cyclic behavior of certainparameters that are extracted by IVUS images. If the behavior of theseparameters are monitored, then the periodicity of the cardiac cycle canbe determined. Knowing the frame acquisition rate will also allow thedetermination of the cardiovascular cycle as a temporal quantity.

The closeness between successive IVUS images is a parameter whichclearly behaves in a periodic pattern. This is a result of theperiodicity of most types of inter-image motion that are present. Acloseness function may be formed in which each value results from acloseness operation between a pair of successive images. For example, aset of ten images will produce nine successive closeness values.

The closeness function can be derived from a cross-correlation typeoperation, SAD operation or any other type of operation that produces acloseness type of function. Normalized cross-correlation produces verygood results when used for monitoring periodicity.

The following formula shows the formula for the cross-correlationcoefficient (as a function of the Nth image) for calculating thecloseness function: ##EQU5## where: Correlation₋₋ function(N)=onedimensional function producing one value for every pair of images;

A=predecessor image matrix (the Nth image);

B=current image matrix (the Nth+l image);

*=multiplication of pixel by corresponding pixel;

Σ=sum on all pixels in matrix.

The correlation coefficient is a byproduct of the stabilization process,because the central value (shiftX=0, shiftY=0) of the normalizedcross-correlation matrix (C(shiftX, shiftY)) is always computed. Thisholds true for all types of closeness functions used for stabilization.The central value of the closeness matrix (C(shiftX=0, shiftY=0)),either cross-correlation or another type of operation used forstabilization, can always be used for producing a closeness function.

The closeness function can also be computed from images which areshifted one in relation to another, i.e., the value used to form thefunction is C(shiftX, shiftY) where shiftX and shiftY are not equal tozero. The Closeness function need not necessarily be formed from wholeimages but can also be calculated from parts of images, eithercorresponding or shifted in relation to one another.

FIG. 9 shows an ECG and cross-correlation coefficient plottedgraphically in synchronous fashion. Both curves are related to the sameset of images. FIG. 9(a) shows a graph of the ECG signal and FIG. 9(b)shows a graph of the cross-correlation coefficient derived fromsuccessive IVUS images. The horizontal axis displays the image number (atotal of 190 successive images). As can be observed, thecross-correlation coefficient function in FIG. 9(b) shows a periodicpattern, and its periodicity is the same as that displayed by the ECGsignal in FIG. 9(a) (both show approximately six heart beats).

Monitoring the periodicity of the closeness function may be complicatedbecause the closeness function does not have a typical shape, it mayvary in time, it depends on the type of closeness function used, and itmay vary from vessel segment to vessel segment and from subject tosubject.

To monitor the periodicity of the closeness function continuously andautomatically a variety of methods may be employed. One method, forexample, is a threshold type method. This method monitors for a value ofthe closeness function over a certain value known as a threshold. Oncethis value is detected, the method monitors for when the threshold isagain crossed. The period is determined as the difference in timebetween the crossings of the threshold. An example of this method isshown in FIG. 10 as a table. The table shows a group ofcross-correlation coefficient values (middle row) belonging tosuccessive images (numbers 1 through 10 shown in the top row). If thethreshold, for example, is set to the value of 0.885, then thisthreshold is first crossed in the passage from image #2 to image #3. Thethreshold is crossed a second time in the passage from image #6 to image#7. Thus, the time period of the periodicity is the time taken toacquire 7-3=4 images.

Another method that can be used to extract the cardiac periodicity fromthe closeness curve is internal cross-correlation. This method utilizesa segment of the closeness function, i.e., a group of successive values.For example, in thc table shown in FIG. 10, the segment may be comprisedof the first four successive images, i.e., images #1 through #4. Once asegment is chosen, it is cross-correlated with itself, producing across-correlation value of 1. Next, this segment is cross-correlatedwith a segment of the same size extracted from the closeness function,but shifted one image forward. This is repeated, with the segmentshifted two images forward, and so on. In the example shown in FIG. 10,the segment {0.8, 0.83, 0.89, 0.85} would be cross-correlated with asegment shifted by one image {0.83, 0.89, 0.85, 0.82}, then the segment{0.8, 0.83, 0.89, 0.85} would be cross-correlated with a segment shiftedby two images {0.89, 0.85, 0.82, 0.87}, and so on. The bottom row of thetable in FIG. 10 shows the results of these internal cross-correlations.The first value of 1 is a result of the cross-correlation of the segmentwith itself. These cross-correlation values are examined to determinethe location of the local maxima. In this example, they are located inimage #1 and image #5 (their values are displayed in bold). Theresulting periodicity is the difference between the location of thelocal maxima and the location from which the search was initiated (i.e.,image #1). In this example, the periodicity is the time that elapsedfrom the acquisition of image #1 to image #5, which is 5-1=4 images.Once a period has been detected, the search begins anew using a segmentsurrounding the local maximum, e.g., image #5. In this example, forexample, the new segment could be the group of closeness valuesbelonging to images #4 through #7.

Due to the nature of the type of calculation involved, the internalcross-correlation operation at a certain point in time requires thecloseness values of images acquired at a future time. Thus, unlike thethreshold method, the closeness method requires the storage of images(in memory) and the periodicity detection is done retrospectively. Thecardiac periodicity can also be monitored by transforming the closenesscurve into the temporal frequency domain by the Fourier transform. Inthe frequency domain the periodicity should be expressed as a peakcorresponding to the periodicity. This peak can be detected usingspectral analysis.

The closeness function can provide additional important informationabout IVUS images which cannot be extracted from external signals, suchas ECG, that are not derived from the actual images. The behavior ofthis function can indicate certain states in the IVUS images or imageparts used to form the closeness function. Important features in thecloseness function which are indicative of the state of the IVUS imagesare the presence of periodicity and the "roughness" of the closenessfunction. Normal IVUS images should exhibit a relatively smooth andperiodic closeness function as displayed, for example, in FIG. 9(b).

However, if "roughness" and/or periodicity are not present then thiscould indicate some problem in the formation of IVUS images, i.e., thepresence of an artifact in the image formation caused by, for example,either a mechanical or electronic malfunction. The following figurehelps to illustrate this. FIG. 11 shows a graph of the cross-correlationcoefficient derived from successive IVUS images. This graph isanalogues, in its formation, to the cross-correlation plot in FIG. 9(b),but in this example it is formed by a different imaging catheter used ina different subject.

In this example, it is clear that the closeness function does notexhibit clear periodicity nor does it have a smooth appearance butrather a rough or spiky appearance. In this case the behavior of thecloseness graph was caused by the non-uniformity of the rotation of theIVUS transducer responsible for emitting/collecting the ultrasonicsignals displayed in the image. This type of artifact sometimes appearsin IVUS catheter-transducer assemblies in which there are movingmechanical parts.

The closeness function, when considered to reflect normal imagingconditions, can serve for a further purpose. This is linked with thelocation of the maxima in each cycle of the closeness function. Locatingthese maxima may be important for image processing algorithms whichprocess several successive images together. Images found near maximaimages tend to have high closeness and little inter-image motion, one inrelation to the other. Additionally, if images belonging to the samephase of successive cardiac cycles are required to be selected, it isusually best to select them using the maxima (of the closeness function)in each cycle.

In one display method, for example, these images are projected onto thedisplay and the gaps are filled in by interpolated images. By thisdisplay method all types of periodic motion can be stabilized.

The shift logic stage in the stabilization process can also make use ofcardiovascular periodicity monitoring. If drift is to be avoided, theaccumulated shift after each (single) cardiac cycle should be small orzero, i.e., the sum of all shifts over a period of a cycle should resultin zero or near zero. This means that the drift phenomena can be limitedby utilizing shift logic which is coupled to the periodicity monitoring.

Referring now to FIG. 12, most IVUS images can be divided into threebasic parts. The central area (around the catheter), labeled as Lumen inFIG. 12, is the actual lumen or interior passageway (cavity) throughwhich fluid, e.g., blood flows. Around the lumen, is the actual vessel,labeled Vessel in FIG. 12, composed of several layers of tissue andplaque (if diseased). Surrounding the vessel is other tissue, labeledExterior in FIG. 12, i.e., muscle or organ tissue, for example, theheart in the coronary vessel image.

When IVUS images are viewed dynamically (i.e., in film format), thedisplay of the interior, where the blood flows, and of the exteriorsurrounding the vessel, usually shows a different temporal behavior thanthe vessel itself.

Automatically monitoring the temporal behavior of pixels in the dynamicIVUS image would allow use of the information extracted by the processto aid in interpretation of IVUS images. This information can be used toenhance IVUS displays by filtering and suppressing the appearance offast changing features, such as fluid, e.g., blood, and the surroundingtissue, on account of their temporal behavior. This information can alsobe used for automatic segmentation, to determine the size of the lumenautomatically by identifying the fluid, e.g., blood, and the surroundingtissue based on the temporal behavior of textural attributes formed bytheir composing pixels.

To accomplish automatic monitoring of temporal behavior there must be anevaluation of the relationship between attributes formed bycorresponding pixels belonging to successive images. Fxtraction oftemporal behavior bears resemblance to the methods used for closenessoperations on a localized basis, as described previously.

High temporal changes are characterized by relatively large relativegray value changes of corresponding pixels, when passing from one imageto the next. These fast temporal changes may be suppressed in thedisplay by expressing these changes through the formation of a maskwhich multiplies the original image. This mask reflects temporal changesin pixel values. A problem that arises in this evaluation is determiningwhether gray value changes in corresponding pixel values are due toeither flow or change in matter, or movements of the vessel/catheter. Byperforming this evaluation on stabilized images overcomes or at leastminimizes this problem.

The following definitions apply:

B=current (stabilized or non-stabilized) image;

A=predecessor (stabilized or non-stabilized) image;

C=successor (stabilized or non-stabilized) image;

abs=absolute value.

The matrices used can be either in Cartesian or Polar form.

The following operation, resulting in a matrix D1, shall be defined asfollows: D1 is a matrix, in which each pixel with coordinates X, Y isthe sum of the absolute differences of its small surroundingneighborhood, e.g., 9 elements (X-2:X+2, Y-2:Y+2-a 3×3 square),extracted from images A and B, respectively.

For example, the following illustration shows corresponding pixels (inbold) and their close neighborhood in matrices A and B.

    ______________________________________                                        A               B        D1                                                   ______________________________________                                        1 4 51          3 6 8                                                         6 7 15          3 4 70   190                                                  3 5 83          2 1 6                                                         ______________________________________                                    

The pixel in matrix D1, with the location corresponding to the pixelswith value 4 (in B) and 7 (in A) will be assigned the following value:

    abs(1-3)+abs(4-6)+abs(51-8)+abs(6-3)+abs(7-4)+abs(15-70)+abs(3-2)+abs(5-1)+abs(83-6)=190

D2 is defined similarly but for matrices B and C.

D1 and D2 are, in effect, difference matrices which are averaged byusing the 3×3 neighborhood in order to diminish local fluctuations ornoise. Large gray value changes between images A and B or between B andC will be expressed as relatively high values in matrices D1 and D2respectively.

A new matrix, Dmax is next formed, in which every pixel is the maximumof the corresponding pixels in matrices D1 and D2:

    Dmax=max(D1, D2)

where:

max(D1, D2)=each pixel in Dmax holds the highest of the twocorresponding pixels in D1 and D2.

Thus, the single matrix Dmax particularly enhances large pixel changesbetween matrices A, B and C. A mask matrix (MD), is then formed fromDmax by normalization, i.e., each pixel in Dmax is divided by themaximal value of Dmax. Therefore, the pixel values of the mask MD rangefrom zero to one.

The role of the mask is to multiply the current image B in the followingmanner, forming a new matrix or image defined as BOUT:

    BOUT=(1-MD.sup.n)*B

where:

B=original current image;

BOUT=the new image;

^(n) =each pixel in the matrix MD is raised to the power of n. n isgenerally a number with a value, for example, of 2-10;

1-MD^(n) =a matrix in which each pixel's value is one minus the value ofthe corresponding pixel in MD.

By performing the subtraction 1-MDn, small values of MD which reflectslow changing features become high values in 1-MD . Moreover, the chancethat only slow changing features will have high values is increasedbecause of the prior enhancement of high MD values (by forming MD as amaximum between matrices D1 and D2).

The multiplication of the mask (1-MDn) by the current image B, forms anew image BOUT in which the appearance of slow changing pixels areenhanced while fast changing pixels' values are decreased. The number ndetermines how strong the suppression of fast changing features willlook on the display.

FIG. 13 illustrates the results of temporal filtering. The left image isan original IVUS image (i.e., matrix B) from a coronary vessel, as itwould look on the current display. The right image has undergone theprocessing steps described above, i.e., temporal filtering (matrixBOUT). Note that in the right image, blood and the surrounding tissue isfiltered (suppressed) and lumen and vessel borders are much easier toidentify.

Automatic segmentation differentiates fluid, e.g., blood and exterior,from the vessel wall based on the differences between the temporalbehavior of a textural quality. As in the case of temporal filtering,this method is derived from the relationship between correspondingpixels from a number of successive images. If pixel values changebecause of inter-image motion, then performance of the algorithm will bedegraded. Performing stabilization prior to automatic segmentation willovercome, or at least minimize this problem.

As in the case of temporal filtering, the following definitions shallapply:

B=current (stabilized or non-stabilized) image;

A=predecessor (stabilized or non-stabilized) image;

C=successor (stabilized or non-stabilized) image.

The matrices can be either in Cartesian or Polar form.

The textural quality can be defined as follows: Suppose the four nearestneighbors of a pixel with value "a" are "b," "c," "d" and "e," then theclassification of "a" will depend on its relations with "b," "c," "d"and "e." This can be shown with the following illustration: ##EQU6##

The following categories can now be formed:

In the vertical direction:

if a>b and a>e then "a" is classified as belonging to the category I;

if a>b and a<e then "a" is classified as belonging to the category II;

if a<b and a<e then "a" is classified as belonging to the category III;

if a<b and a>e then "a" is classified as belonging to the category IV;

if a=b or a=e then "a" is classified as belonging to the category V.

In the horizontal direction:

if a>c and a>d then "a" is classified as belonging to the category I;

if a>c and a<d then "a" is classified as belonging to the category II;

if a<c and a<d then "a" is classified as belonging to the category III;

if a<c and a>d then "a" is classified as belonging to the category IV;

if a=c or a=d then "a" is classified as belonging to the category V.

The vertical and horizontal categories are next combined to form a newcategory. As a result, pixel "a" can now belong to 5×5=25 possiblecategories. This means that the textural quality of "a" is characterizedby its belonging to one of those (25) categories.

For example, in the following neighborhood: ##EQU7##

Pixel "a"=10 is classified as belonging to the category which includescategory I vertical (because 10>7 and 10>3) and category V horizontal(because 10=10). However, if pixel "a" would have been situated in thefollowing neighborhood: ##EQU8## it would have been classified asbelonging to a different category because its horizontal category is nowcategory III (10<11 and 10<14).

By determining the relationship of each pixel to its close neighborhooda textural quality has been formed which classifies each pixel into 25possible categories. The number of categories may vary (increased ordecreased), i.e., for example, by changing the categorizing conditions,as may the number of close neighbors used, for example, instead of four,eight close neighbors may be used.

The basic concept by which the textural changes are used todifferentiate fluid, e.g., blood, from the vessel is by monitoring thechange in categories of corresponding pixels in successive images. Toaccomplish this the category in each and every pixel in matrices A, Band C are determined. Next, corresponding pixels are each tested to seeif this category has changed. If it has, the pixel is suspected of beinga fluid, e.g., blood, or surrounding tissue pixel. If it has notchanged, then the pixel is suspected of being a vessel pixel.

The following example shows three corresponding pixels (with values 8,12 and 14) and their neighborhoods in successive matrices A, B and C.

    ______________________________________                                        A             B          C                                                    ______________________________________                                          5             9          1                                                  9 8  11       19 12  13  21 14 17                                               23            100        20                                                 ______________________________________                                    

In this example, the category of the pixel valued 12 (in B) is the sameas in A and C, so it will be classified as a pixel with a higher chanceof being a vessel wall pixel. If, however, the situation was as shownbelow (20 in C changes to 13):

    ______________________________________                                        A             B          C                                                    ______________________________________                                          5             9          1                                                  9 8  11       19 12  13  21 14 17                                               23            100        13                                                 ______________________________________                                    

then pixels 8 in A and 12 in B have the same categories, but 14 in C hasa different category as in the prior example. As a result, pixel 12 in Bwill be classified as a pixel with a higher chance of being a fluid(lumen), i.e., blood, or exterior tissue pixel.

The classification method described so far monitors the change in thetexture or pattern associated with the small neighborhood around eachpixel. Once this change is determined as described above, each pixel canbe assigned a binary value. For example, a value of 0, if it issuspected to be a vessel pixel, or a value of 1, if it is suspected tobe a blood pixel or a pixel belonging to the vessel's exterior. Thebinary image, serves as an input for the process of identification ofthe lumen and the original pixel values cease to play a role in thesegmentation process.

Identification of the lumen using the binary image is based on twoassumptions which are generally valid in IVUS images processed in themanner described above. The first, is that the areas in the image whichcontain blood or are found on the exterior of the vessel arecharacterized by a high density of pixels with a binary value of 1 (or alow density of pixels with a value of zero). The term density is neededbecause there are always pixels which are misclassified. The secondassumption, is that from a morphological point of view, connected areasof high density of pixels with the value of 1 (lumen) should be foundaround the catheter and surrounded by connected areas of low density ofpixels with the value of 1 (vessel) which are in turn, surrounded againby connected areas of high density of pixels with the value of 1(vessel's exterior). The reason for this assumption is the typicalmorphological arrangement expected from a blood vessel.

These two assumptions form the basis of the subsequent processingalgorithm which extracts the actual area associated with the lumen outof the binary image. This algorithm can utilize known image processingtechniques, such as thresholding the density feature in localizedregions (to distinguish blood/exterior from vessel ) and morphologicaloperators such as dilation or linking to inter-connect and form aconnected region which should represent the actual lumen found withinthe vessel wall limits.

FIG. 14 shows an image of the results of the algorithm for automaticextraction of the lumen. The image is an original IVUS image (forexample, as described above as image B) and the lumen borders aresuperimposed (by the algorithm) as a bright line. The algorithm for theextraction of the lumen borders was based on the monitoring of thechange in the textural quality described above, using three successiveimages.

The examples described above of temporal filtering and automaticsegmentation include the use of two additional images (for example, asdescribed above as images A and C) in addition to the current image (forexample, as described above as image B). However, both of these methodscould be modified to utilize less (i.e., only one additional image) ormore additional images.

The performance of the two methods described above will be greatlyenhanced if combined with cardiovascular periodicity monitoring. Thisapplies, in particular, to successive images in which cardiovascularperiodicity monitoring produces high inter-image closeness values. Thoseimages usually have no inter-image motion. Thus, most reliable resultscan be expected when successive images with maximal inter-imagecloseness are fed as inputs to either temporal filtering or automaticsegmentation.

During treatment of vessels using catheterization, it is a commonpractice to repeat IVUS pullback examinations in the same vesselsegment. For example, a typical situation is first to review the segmentin question, evaluate the disease (if any), remove the IVUS catheter,consider therapy options, perform therapy and then immediately after(during the same session) examine the treated segment again using IVUSin order to assess the results of therapy.

To properly assess the results of such therapy, corresponding segmentsof the pre-treatment and post-treatment segments which lie on the samelocations along the length of the vessel, i.e., corresponding segments,should be compared. The following method provides for matching, i.e.,automatic identification (registration) of corresponding segments.

To accomplish matching of corresponding segments, closeness/similarityoperations are applied between images belonging to a first group ofsuccessive images, i.e., a reference segment, of a first pullback filmand images belonging to a second group of successive images of a secondpullback film. Matching of the reference segment in the first film toits corresponding segment in the second film is obtained when somecriteria function is maximized.

From either one of the two films a reference segment is chosen. Thereference segment may be a group of successive images representing, forexample, a few seconds of film of an IVUS image. It is important toselect the reference segment from a location in a vessel which ispresent in the two films and has undergone no change as a result of anyprocedure, i.e., the reference segment is proximal or distal to thetreated segment.

As an example, the table in FIG. 15 will help clarify the method formatching of corresponding segments.

The left column shows the time sequence of the first film, in this casethe film consists of twenty successive images. The middle column, showsthe reference segment which is selected from the second film andconsists of 10 successive images. The right column lists the 10successive images from the first film (#5--14) which actually correspondto (or match) the images of the reference segment from the second film(#1-#10). The purpose of the matching process is to actually reveal thiscorrespondence.

Once a reference segment is chosen, it is shifted along the other film,one image (or more) each time, and a set of stabilization and closenessoperations are performed between the corresponding images in eachsegment. The direction of the shift depends on the relative location ofthe reference segment in the time sequence of the two films. However, ingeneral, if this is not known, the shift can be performed in bothdirections.

In the example of FIG. 5:

r=reference segment; and

f=first film,

the first set of operations will take place between the imagescomprising the following pairs: r#1--f#1, r#2--f#2, r#3--f#3, . . . ,r#10--f#10.

The second set of operations will take place between the imagescomprising the following pairs: r#1-f#2, r#2-f#3, r#3-f#4, . . . ,r#10-f#11.

The third set of operations will take place between the imagescomprising the following pairs: r#1-f#3, r#2-f#4, r#3-f#5, . . . ,f#10-f#12, and so on, etc. As can be observed in this example, theshifting is performed, by a single image each time and in one directiononly.

For example, the following operations between the images in each pairmay be performed. First, an image from the reference segment isstabilized for rotational and Cartesian motion, in relation to itscounterpart in the first film. Then closeness operations are performedbetween the images in each pair. This operation can be, for example,normalized cross-correlation (discussed above in relation to periodicitydetection). Each such operation produces a closeness value, for example,a cross-correlation coefficient when normalized cross-correlation isused. A set of such operations will produce a number ofcross-correlation values. In the example shown in the table of FIG. 15,each time the reference segment is shifted, ten new cross-correlationcoefficients will be produced.

The closeness values produced by a set of operations can then be mappedinto some type of closeness function, for example, an average function.Using the above example, the cross-correlation coefficients are summedup and then divided by the number of pairs, i.e., ten. Each set ofoperations results therefore, in a single value, i.e., an averagecloseness, which should represent the degree of closeness between thereference segment and its temporary counterpart in the first film. Thus,the result of the first set of operations will be a single value, theresult of the second set of operations will be another value, etc.

We can expect that the maximal average closeness will occur as a resultof the operations performed between segments which are very alike, i.e.,corresponding or matching segments.

In the above example of FIG. 15, these segments should be matched duringthe fifth set of operations which take place between the imagescomprising the following pairs: r#1-f#5, r#2-f#6, r#3-f#7, . . . ,r#10-f#14.

The maximal average closeness should, therefore, indicate correspondingsegments because each pair of images are, in fact, corresponding images,i.e., they show the same morphology. The criteria might not, however,follow this algorithm. It may, for example, take into account the formof the closeness function, derived from many shifted segment positionsinstead of using only one of its values which turns out to be themaximum.

Once corresponding segments are identified, the complete first andsecond films may be synchronized one in relation to the other. This willbe a result of an appropriate frame shift, revealed by the matchingprocess, implemented in one film in relation to the other. Thus, whenwatching the two films side by side, the pre-treated segment will appearconcurrently with the post-treated section.

Besides for synchronizing the corresponding segments, the aboveoperation also stabilizes the corresponding segments one in relation tothe other. This further enhances the ability to understand the changesin morphology. Thus, even though when the catheter is reinserted in thevessel its position and orientation are likely to have changed,nevertheless, the images in the pre-treatment and post-treatment filmswill be stabilized in relation to each other.

The number of images used for the reference segment may vary. The moreimages used in the matching process, the more robust and less prone tolocal errors it will be. However, the tradeoff is more computationaltime required for the calculations for each matching process as thenumber of pairs increases.

It is important in acquiring the pullback films that the pullback rateremains stable and is known. It is preferred that the pullback rate beidentical in the two acquisitions.

Many different variations of the present invention are possible. Thevarious features described above may be incorporated individually andindependently of one another. These features may also be combined invarious groupings.

What is claimed is:
 1. A method for intravascular ultrasound imaging, comprising the steps of:placing an ultrasound signal transmitter and detector, within a bodily lumen; detecting ultrasound signals; deriving a first image from a first set of detected ultrasound signals; processing and digitizing the first set of detected ultrasound signals; deriving a first two-dimensional array from the digitized first set of detected ultrasound signals, the first two-dimensional array including a first plurality of elements wherein the first image is configured as the first two-dimensional array; deriving a second image from a second set of the detected ultrasound signals; processing and digitizing the second set of detected ultrasound signals; deriving a second two-dimensional array from the digitized second set of detected ultrasound signals, wherein the second image is configured as the second two-dimensional array, the second two-dimensional array including a second plurality of elements, each one of the first plurality of elements and the second plurality of elements representing a detected ultrasound signal from a predetermined spatial location; and performing a shift evaluation of the second image in relation to the first image in order to detect a motion.
 2. The method according to claim 1, wherein:each one of the first two dimensional array and the second two dimensional array is configured in polar coordinates, and the shift evaluation is performed in polar coordinates along at least one dimension.
 3. The method according to claim 1, wherein the step of performing the shift evaluation includes performing at least one closeness operation.
 4. The method according to claim 3, wherein the at least one closeness operation includes at least one of cross-correlation, normalized cross-correlation and SAD.
 5. The method according to claim 4, wherein the cross-correlation includes at least one of direct cross-correlation and Fourier transform.
 6. The method according to claim 1, wherein:each one of the first two dimensional array and the second two dimensional array is configured in Cartesian coordinates, and the shift evaluation is performed in Cartesian coordinates along at least one dimension.
 7. The method according to claim 2, wherein the detected motion corresponds to at least one of a rotational movement and a vasomotion.
 8. The method according to claim 7, wherein:the rotational movement corresponds to at least one of a global rotational movement, a rigid rotational movement and a local rotational movement, and the vasomotion corresponds to at least one of a global vasomotion and a local vasomotion.
 9. The method according to claim 6, wherein the detected motion corresponds to a Cartesian displacement.
 10. The method according to claim 9, wherein the Cartesian displacement corresponds to at least one of a rigid Cartesian displacement and a local Cartesian displacement.
 11. A method for intravascular ultrasound imaging, comprising the steps of:placing an ultrasound signal transmitter and detector within a bodily lumen; detecting a plurality of ultrasound signals; deriving successive sets of ultrasound signals from the plurality of ultrasound signals; deriving a plurality of successive images from the successive sets of ultrasound signals; and monitoring for a change in corresponding pixel values belonging to successive images in the plurality of images.
 12. The method according to claim 11, further comprising the step of image enhancement by filtering the change in the corresponding pixel values.
 13. The method according to claim 11, further comprising the steps of:monitoring for a change in a texture of the corresponding pixel values; and performing an automatic image segmentation including a lumen identification.
 14. A method for intravascular ultrasound imaging, comprising the steps of:placing an ultrasound signal transmitter and detector, within a bodily lumen; detecting a plurality of ultrasound signals; deriving successive sets of ultrasound signals from the plurality of ultrasound signals; deriving a plurality of successive images from the successive sets of ultrasound signals; evaluating each pair of successive images; and deriving a closeness function, wherein:for each pair of successive images, the step of evaluating includes the step of performing a closeness operation between a first image of the pair of images and a second image of the pair of images, and each value of the closeness function is a result of the performed closeness operation.
 15. The method according to claim 14, wherein the closeness operation corresponds to at least one of cross-correlation, normalized cross-correlation and SAD.
 16. A method for intravascular ultrasound imaging, comprising the steps of:placing an ultrasound signal transmitter and detector within a bodily lumen; detecting a plurality of ultrasound signals; deriving successive sets of ultrasound signals from the plurality of ultrasound signals; deriving a plurality of successive images from the successive sets of ultrasound signals; and performing a shift evaluation for each image in relation to a predecessor image in order to monitor each one of the successive images for a vasomotion of the bodily lumen.
 17. The method according to claim 16, wherein the vasomotion corresponds to at least one of a local vasomotion and a global vasomotion.
 18. The method according to claim 14, wherein the closeness function is monitored for cardiovascular periodicity.
 19. The method according to claim 18, wherein the monitoring of the closeness function includes at least one of a threshold crossing, an internal closeness function, a Fourier transform, and a spectral analysis.
 20. The method according to claim 14, wherein the closeness function is analyzed for indicating a quality of at least one image of the plurality of successive images used to form the closeness function.
 21. The method according to claim 14, wherein, for each pair of successive images, the step of evaluating further includes the step of performing a shift evaluation between the first image of the pair of images and the second image of the pair of images.
 22. The method according to claim 7, further comprising the step of stabilizing the second image in relation to the first image for at least one of the rotational movement and the vasomotion, wherein the stabilizing step is performed in polar coordinates.
 23. The method according to claim 9, further comprising the step of stabilizing the second image in relation to the first image for the Cartesian displacement, wherein the stabilizing step is performed in Cartesian coordinates.
 24. The method according to claim 23, further comprising the step of displaying the first image and the stabilized second image.
 25. The method according to claim 22, wherein the stabilizing step stabilizes at least one of a global rotation, a rigid rotation, a local rotation, a local vasomotion, and a global vasomotion.
 26. The method according to claim 25, wherein the step of stabilizing at least one of the global rotation and the rigid rotation and the global vasomotion includes the step of shifting the second image in polar coordinates along at least one dimension according to a magnitude and a direction derived from a result of the shift evaluation.
 27. The method according to claim 26, further comprising the step of limiting a drift occurring due to the stabilizing of the second image in relation to the first image.
 28. The method according to claim 27, wherein the step of limiting the drift includes the step of shifting the second image by a magnitude that is adjusted by using information derived from a cardiovascular periodicity monitoring.
 29. A method for intravascular ultrasound imaging, comprising the steps of:placing an ultrasound signal transmitter and detector, within a bodily lumen; detecting a plurality of ultrasound signals; deriving a plurality of images from the plurality of detected ultrasound signals; performing a shift evaluation for each image in relation to a predecessor image; monitoring for a change in corresponding pixel values belonging to successive images in the plurality of images; monitoring cardiovascular periodicity; and stabilizing each image of the plurality of images in relation to the predecessor image.
 30. A method for intravascular ultrasound imaging, comprising the steps of:placing an ultrasound signal transmitter and detector, within a bodily vessel; moving the ultrasound signal transmitter and detector through a section of the bodily vessel; detecting ultrasound signals; deriving a first image from ultrasound signals detected during a first movement of the ultrasound signal transmitter and detector through the vessel; deriving a second image from ultrasound signals detected during a second movement of the ultrasound signal transmitter and detector through the vessel; performing a shift evaluation and a stabilization on the second image in relation to the first image; and displaying the stabilized second image.
 31. A method for intravascular ultrasound imaging, comprising the steps of:placing an ultrasound signal transmitter and detector, within a bodily vessel; moving the ultrasound signal transmitter and detector through a section of the bodily vessel; detecting ultrasound signals; deriving a first image from ultrasound signals detected from a first portion of the vessel; deriving a second image from ultrasound signals detected from a second portion of the vessel; performing a shift evaluation and a stabilization on the second image in relation to the first image; and displaying the stabilized second image.
 32. A method for intravascular ultrasound imaging, comprising the steps of:placing an ultrasound signal transmitter and detector, within a bodily vessel; performing a first movement of the ultrasound signal transmitter and detector along a first section of the vessel; detecting a first set of ultrasound signals; deriving a first series of successive images from the first set of detected ultrasound signals; performing a second movement of the ultrasound signal transmitter and detector along a second section of the vessel; detecting a second set of ultrasound signals; deriving a second series of successive images from the second set of detected ultrasound signals; selecting from the second series of successive images a subgroup of successive images formed as a reference segment; performing a closeness operation between each image of the reference segment and each image of a plurality of subgroups of images derived from the first series of successive images; and matching each image of the reference segment to each image of another subgroup of the plurality of subgroups the other subgroup being a matched segment.
 33. The method according to claim 32, wherein:each image of the reference segment is associated by the step of matching with a counterpart image from the matched segment, and each image of the reference segment covers substantially the same predetermined location of the vessel as the associated counterpart image.
 34. The method according to claim 32, wherein the step of matching includes the step of performing a relative shift of the reference segment by at least a single image in relation to the first series of successive images, wherein each shift results in closeness operations being performed between images of a new subgroup of the plurality of subgroups derived from the first series of successive images and between the images of the reference segment.
 35. The method according to claim 34, wherein the step of matching further includes the step of stabilizing each image of the reference segment in relation to each counterpart image of the new subgroup.
 36. The method according to claim 33, further comprising the step of stabilizing each image of the reference segment in relation to each counterpart image of the matched segment.
 37. The method according to claim 32, further comprising the step of stabilizing each image of the second series of successive images in relation to each counterpart image of the first series of successive images.
 38. The method according to claim 32, wherein the closeness operation includes one of cross-correlation and normalized cross-correlation.
 39. The method according to claim 32, wherein the first section and the second section of the bodily lumen are approximately coextensive.
 40. The method according to claim 1, wherein the ultrasound signal transmitter and detector is coupled to a probe.
 41. The method according to claim 40, wherein the probe is at least one of a catheter and a guide wire.
 42. The method according to claim 1, wherein the ultrasound signal transmitter and detector includes an independent transmitter and an independent detector.
 43. The method according to claim 14, wherein the ultrasound signal transmitter and detector includes an independent transmitter and an independent detector.
 44. The method according to claim 30, wherein the ultrasound signal transmitter and detector is coupled to a probe, the probe moving the ultrasound signal transmitter and detector.
 45. The method according to claim 44, wherein the probe is at least one of a catheter and a guide wire.
 46. The method according to claim 30, wherein the ultrasound signal transmitter and detector includes an independent transmitter and an independent detector.
 47. The method according to claim 31, wherein the ultrasound signal transmitter and detector is coupled to a probe, the probe moving the ultrasound signal transmitter and detector.
 48. The method according to claim 47, wherein the probe is at least one of a catheter and a guide wire.
 49. The method according to claim 31, wherein the ultrasound signal transmitter and detector includes an independent transmitter and an independent detector.
 50. The method according to claim 32, wherein the ultrasound signal transmitter and detector is coupled to a probe, the probe moving the ultrasound signal transmitter and detector.
 51. The method according to claim 50, wherein the probe is at least one of a catheter and a guide wire.
 52. The method according to claim 32, wherein the ultrasound signal transmitter and detector includes an independent transmitter and an independent detector.
 53. The method according to claim 23, wherein:the Cartesian displacement includes one of a global Cartesian displacement, a local Cartesian displacement, and a rigid Cartesian displacement, and the step of stabilizing includes the step of stabilizing at least one of the global Cartesian displacement, the rigid Cartesian displacement, and the local Cartesian displacement.
 54. The method according to claim 53, wherein the step of stabilizing at least one of the global Cartesian displacement and the rigid Cartesian displacement is performed by shifting the second image in Cartesian coordinates along at least one dimension according to a magnitude and a direction derived from a result of the shift evaluation.
 55. The method according to claim 54, further comprising the step of limiting a drift occurring due to the stabilizing of the second image in relation to the first image.
 56. The method according to claim 55, wherein the step of limiting the drift includes the step of shifting the second image by a magnitude that is adjusted by using information derived from a cardiovascular periodicity monitoring.
 57. A method for performing an IVUS imaging operation, comprising the steps of:placing within a bodily lumen an ultrasound signal transmitter and an ultrasound signal detector; detecting a plurality of ultrasound signals reflected from at least a portion of the bodily lumen; processing and digitizing a first set of ultrasound signals obtained from the plurality of reflected ultrasound signals in order to produce at least a first set of digitized samples and a second set of digitized samples; deriving from the first set of digitized samples a first two dimensional array including a set of data, wherein:the first two dimensional array includes a first plurality of elements, each of the first plurality of elements representing a digitized sample corresponding to one of the plurality of reflected ultrasound signals from a predetermined spatial location of the bodily lumen, the set of data of the first two dimensional array representing a complete cross section of the bodily lumen, the first two dimensional array is configured in polar coordinates, a first axis of the first two dimensional array represents an r coordinate, and a second axis of the first two dimensional array represents an angular coordinate; deriving from the second set of digitized samples a second two dimensional array including a set of data, wherein:the second two dimensional array includes a second plurality of elements, each of the second plurality of elements representing a digitized sample corresponding to one of the plurality of reflected ultrasound signals from the predetermined spatial location, the set of data of the second two dimensional array represents the complete cross section of the bodily lumen, the second two dimensional array is configured in polar coordinates, a first axis of the second two dimensional array represents the r coordinate, a second axis of the second two dimensional array axis represents the angular coordinate; performing a shift evaluation and detection operation of the second two dimensional array with respect to the first two dimensional array in order to detect a magnitude and a direction of at least one of a global vasomotion and a rigid rotation represented by the set of data corresponding to the second two dimensional array in relation to the set of data corresponding to the first two dimensional array; and stabilizing the second two dimensional array by applying a shift having a selected magnitude and a selected direction to the second two dimensional array in order to uniformly shift along at least one of the first axis and the second axis of the second two dimensional array each of the second plurality of elements according to the selected magnitude and the selected direction, the stabilizing step providing a compensation for at least one of the rigid rotation and the global vasomotion, wherein the selected magnitude and the selected shift direction are derived from the shift evaluation and detection operation.
 58. The method according to claim 57, wherein the step of performing the shift evaluation and detection operation includes the steps of:providing a first complex two dimensional array having a first plurality of complex elements by applying a two dimensional Fourier transform to the first two dimensional array, providing a second complex two dimensional array having a second plurality of complex elements by applying a two dimensional Fourier transform to thc second two dimensional array, obtaining a conjugate of the second plurality of elements, multiplying each one of the first plurality of complex elements with a corresponding one of the conjugate of the second plurality of complex elements, providing a result of each multiplication in a third two dimensional array, each complex data element of the third complex two dimensional array being a product of a multiplication between an element of the first complex two dimensional array and a corresponding conjugate element of the second complex two dimensional array, performing an inverse two dimensional Fourier transform on the third complex two dimensional array, obtaining a real component from each element of the inverse Fourier transformed third complex two dimensional array, providing each obtained real component in a fourth two dimensional array, and finding a location of a global maximum of the fourth two dimensional array, wherein the magnitude and the direction of at least one of the rigid rotation and the global vasomotion are indicated by the location of the global maximum along at least one of a first axis and a second axis of the fourth two dimensional array.
 59. The method according to claim 57, wherein the step of performing the shift evaluation and detection operation includes the steps of:a) uniformly shifting each element of the second two dimensional array along the first axis of the second two dimensional array by a first predetermined magnitude in a first predetermined direction, b) uniformly shifting each element of the second two dimensional array along the second axis of the second two dimensional array by a second predetermined magnitude in a second predetermined direction, c) performing a closeness operation between corresponding elements of the shifted second two dimensional array and the first two dimensional array, d) providing the value resulting from the closeness operation as an element of a third two dimensional array, and e) repeating steps (a) to (d), each repetition being performed by shifting each element of the second two dimensional array along the first axis by a new first magnitude in a new first direction and along the second axis by a new second magnitude in a new second direction, until a location of a global extremum of all possible values of the third two dimensional array is established, wherein the magnitude and the direction of at least one of the rigid rotation and the global vasomotion are indicated by the location of the global extremum along at least one of a first axis and a second axis of the third two dimensional array.
 60. The method according to claim 7, further comprising the step of:reducing the number of repetitions of steps (a) to (d) by performing at least one of the following steps:before step a), performing a sampling operation of the first two dimensional array and the second two dimensional array, and initiating a search of the global extremum of the third two dimensional array, with each initial magnitude of the shift along the first axis and along the second axis of the second two dimensional array being equal to zero, wherein a detected local extremum of the third two dimensional array corresponds to the global extremum of the third two dimensional array.
 61. A method for performing an IVUS imaging operation, comprising the steps of:placing within a bodily lumen an ultrasound signal transmitter and an ultrasound signal detector; detecting a plurality of ultrasound signals reflected from at least a portion of the bodily lumen; processing and digitizing a first set of ultrasound signals obtained from the plurality of reflected ultrasound signals in order to produce at least a first set of digitized samples and a second set of digitized samples; deriving from the first set of digitized samples a first two dimensional array including a set of data, wherein:the first two dimensional array includes a first plurality of elements, each of the first plurality of elements representing a digitized sample corresponding to one of the plurality of reflected ultrasound signals from a predetermined spatial location of the bodily lumen, the set of data of the first two dimensional array representing a complete cross section of the bodily lumen, the first two dimensional array is configured in Cartesian coordinates, a first axis of the first two dimensional array represents an X coordinate, and a second axis of the first two dimensional array represents a Y coordinate; deriving from the second set of digitized samples a second two dimensional array including a set of data, wherein: the second two dimensional array includes a second plurality of elements, cach of the second plurality of elements representing a digitized sample corresponding to one of the plurality of reflected ultrasound signals from the predetermined spatial location, the set of data of the second two dimensional array represents the complete cross section of the bodily lumen, the second two dimensional array is configured in Cartesian coordinates, a first axis of the second two dimensional array represents the X coordinate, and a second axis of the second two dimensional array axis represents the Y coordinate; performing a shift evaluation and detection operation of the second two dimensional array with respect to the first two dimensional array in order to detect a magnitude and a direction of a rigid Cartesian displacement along at least one of the X coordinate and the Y coordinate represented by the set of data corresponding to the second two dimensional array in relation to the set of data corresponding to first two dimensional array; and stabilizing the second two dimensional array by applying a shift having a selected magnitude and a selected direction to the second two dimensional array in order to uniformly shift along at least one of the first axis and the second axis of the second two dimensional array each of the second plurality of elements according to the selected magnitude and the selected direction, the stabilizing step providing a compensation for the rigid Cartesian displacement, wherein the selected magnitude and the selected direction are derived from the shift evaluation and detection operation.
 62. The method according to claim 61, further comprising the step of:displaying an image corresponding to the first two dimensional array and an image corresponding to the stabilized second two dimensional array.
 63. The method according to claim 61, wherein the step of performing the shift evaluation and detection operation includes the steps of:providing a first complex two dimensional array having a first plurality of complex elements by applying a two dimensional Fourier transform to the first two dimensional array, providing a second complex two dimensional array having a second plurality of complex elements by applying a two dimensional Fourier transform to the second two dimensional array, obtaining a conjugate of the second plurality of elements, multiplying each one of the first plurality of complex elements with a corresponding one of the conjugate of the second plurality of complex elements, providing a result of each multiplication in a third two dimensional array, wherein each complex data element of the third complex two dimensional array is a product of a multiplication between an element of the first complex two dimensional array and a corresponding conjugate element of the second complex two dimensional array, performing an inverse two dimensional Fourier transform on the third complex two dimensional array, obtaining a real component from each element of the inverse Fourier transformed third complex two dimensional array, providing each obtained real component in a fourth two dimensional array, and finding a location of a global maximum of the fourth two dimensional array, wherein the magnitude and the direction of the rigid Cartesian displacement along at least one of the X axis and the Y axis are indicated by the location of the global extremum along at least one of a first axis and a second axis of the fourth two dimensional array.
 64. The method according to claim 61, wherein the step of performing the shift evaluation and detection operation includes the steps of:a) uniformly shifting each element of the second two dimensional array along the first axis of the second two dimensional array by a first predetermined magnitude in a first predetermined direction, b) uniformly shifting each element of the second two dimensional array along the second axis of the second two dimensional array by a second predetermined magnitude in a second predetermined direction, c) performing a closeness operation between corresponding elements of the shifted second two dimensional array and the first two dimensional array, d) providing the value resulting from the closeness operation as an element of a third two dimensional array, and e) repeating steps (a) to (d), each repetition being performed by shifting each element of the second two dimensional array along the first axis by a new first magnitude in a new first direction and along the second axis by a new second magnitude in a new second direction, until a location of a global extremum of all possible values of the third two dimensional array is established, wherein the magnitude and the direction of the rigid Cartesian displacement along at least one of the X axis and the Y axis are indicated by the location of the global extremum along at least one of a first axis and one of a second axis of the third two dimensional array.
 65. The method according to claim 64, further comprising the step of:reducing the number of repetitions of steps (a) to (d) by performing at least one of the following steps:before step a), performing a sampling operation of the first two dimensional array and the second two dimensional array, and initiating a search of the global extremum of the third two dimensional array, with each initial magnitude of the shift along the first axis and along the second axis of the second two dimensional array being equal to zero, and wherein a detected local extremum of the third two dimensional array corresponds to the global extremum of the third two dimensional array.
 66. The method according to claim 57, further comprising the steps of:converting the first two dimensional array into a third two dimensional array including a set of data and configured in Cartesian coordinates, wherein:a first axis of the third two dimensional array corresponds to an X coordinate, and a second axis of the third two dimensional array corresponds to a Y coordinate; converting the stabilized second two dimensional array into a fourth two dimensional array including a set of data configured in Cartesian coordinates, the fourth two dimensional array including a plurality of elements, a first axis of the fourth two dimensional array corresponding to the X coordinate, and a second axis of the fourth two dimensional array corresponding to the Y coordinate; performing a shift evaluation and detection operation of the fourth two dimensional array with respect to the third two dimensional array in order to detect a magnitude and a direction of a rigid Cartesian displacement along at least one of the X coordinate and the Y coordinate represented by the set of data corresponding to the fourth two dimensional array in relation to the set of data corresponding to the third two dimensional array; stabilizing the fourth two dimensional array by applying a shift having a selected magnitude and a selected direction to the fourth two dimensional array in order to uniformly shift along at least one of the X coordinate and the Y coordinate each of the plurality of elements of the fourth two dimensional array according to the selected magnitude and the selected direction, the producing step providing a compensation for the rigid Cartesian displacement, wherein the selected magnitude and the selected shift direction are derived from the step of performing the shift evaluation and detection operation of the fourth two dimensional array with respect to the third two dimensional array.
 67. The method according to claim 66, further comprising the step of:displaying an image corresponding to the third two dimensional array and an image corresponding to the stabilized fourth two dimensional array.
 68. A method for forming and processing a closeness function in order to monitor a cardiovascular periodicity from an IVUS imaging operation, comprising the steps of:placing within a bodily lumen an ultrasound signal transmitter and an ultrasound signal detector; detecting a plurality of ultrasound signals reflected from at least a portion of the bodily lumen; processing and digitizing a first set of ultrasound signals obtained from the plurality of reflected ultrasound signals in order to produce at least a first plurality of successive frames, each frame including a set of data, wherein:each frame includes a plurality of elements, each one of the plurality of elements represents a digitized sample corresponding to one of the plurality of reflected ultrasound signals from a predetermined spatial location of the bodily lumen, the set of data corresponding to each one of the plurality of frames representing a complete cross section of the bodily lumen, and each one of the plurality of frames is configured in at least one of Cartesian coordinates and polar coordinates; providing a one dimensional function formed as a closeness function, wherein a value of the closeness function corresponding to f(n), n being a positive integer, is determined by a closeness operation performed between elements stored in the nth frame and between elements stored in the nth+1 frame; and processing the closeness function in order to derive a magnitude of a periodicity of the closeness function.
 69. The method according to claim 68, wherein:the bodily lumen is a blood vessel, and the magnitude of the periodicity of the closeness function corresponds to a magnitude of the cardiovascular periodicity.
 70. The method according to claim 69, wherein the magnitude of the cardiovascular periodicity enables performance of a cardiac gating operation with respect to a cardiovascular cycle.
 71. The method according to claim 68, wherein:an image for display corresponds to one frame of the first plurality of frames used to form the closeness function, and an absence of the periodicity of the closeness function indicates an existence of an artifact in the image for display.
 72. The method according to claim 71, wherein the artifact is caused by a non uniformity of a rotation of at least one of the ultrasound signal transmitter and the ultrasound signal detector within the bodily lumen.
 73. A method for automatically segmenting an IVUS image, comprising the steps of:placing within a bodily lumen an ultrasound signal transmitter and an ultrasound signal detector; detecting a plurality of ultrasound signals reflected from at least a portion of the bodily lumen; processing and digitizing a first set of ultrasound signals obtained from the plurality of reflected ultrasound signals in order to produce a plurality of sets of digitized samples; deriving a plurality of successive frames from the plurality of sets of digitized samples, wherein:each frame includes a plurality of pixels; each pixel represents one of the plurality of digitized samples corresponding to one of the plurality of reflected ultrasound signals from a predetermined spatial location of the bodily lumen, each frame representing a complete cross section of the bodily lumen, and each frame is configured in at least one of polar coordinates and Cartesian coordinates; assigning one of a plurality of textural categories to each pixel included in each frame based on a relationship of each pixel with an adjacent pixel, each assigned textural category being derived from the corresponding pixel being one of higher than, lower than, and equal to in value with respect to each closest adjacent pixel; detecting a change in the assigned textural category of each pixel by monitoring the textural category assigned to each pixel included in each one of the plurality of frames; and producing a binary frame by performing the following steps:classifying each pixel corresponding to a selected one of the plurality frames into one of a first class and a second class, and assigning a binary value to each pixel corresponding to the selected frame, wherein the first class indicates an absence of a change in the textural category assigned to the associated pixel, and wherein the second class indicates a presence of a change in the textural category assigned to the associated pixel.
 74. The method according to claim 73, wherein:the bodily lumen is a blood vessel, and each pixel corresponding to each frame represents one of a blood flow through the blood vessel, a vessel tissue of the blood vessel, and an exterior tissue surrounding the blood vessel, each pixel associated with the first class represents the vessel tissue, each pixel associated with the second class represents one of the blood flow and the exterior tissue, and the binary frame is further processed in order to extract that region of pixels associated with the blood flow through the blood vessel in the selected frame.
 75. A method for enhancing a quality of an IVUS imaging operation, comprising the steps of:placing within a bodily lumen an ultrasound signal transmitter and an ultrasound signal detector; detecting a plurality of ultrasound signals reflected from at least a portion of the bodily lumen; processing and digitizing a first set of ultrasound signals obtained from the plurality of reflected ultrasound signals in order to produce a plurality of sets of digitized samples; deriving a plurality of successive frames from the plurality of sets of digitized samples, wherein:each frame includes a plurality of pixels, each pixel represents one of the plurality of digitized samples corresponding to one of the plurality of reflected ultrasound signals from a predetermined spatial location of the bodily lumen, each frame representing a complete cross section of the bodily lumen, and each frame is configured in at least one of polar coordinates and Cartesian coordinates; deriving a first difference frame having a plurality of pixels, each pixel of the first difference frame including a pixel value, wherein each pixel value corresponds to a sum of absolute differences between pixels included in a first frame of the plurality of frames and between corresponding pixels included in a second frame of the plurality of frames; deriving a second difference frame having a plurality of pixels, each pixel of the second difference frame including a pixel value, wherein each pixel value of each pixel of the second difference frame corresponds to the sum of absolute differences between pixels included in the second frame of the plurality of frames and between corresponding pixels included in a third frame of the plurality of frames; deriving a maximum value frame having a plurality of pixels, each pixel of the maximum value frame including a pixel value , wherein each pixel value of each pixel of the maximum value frame corresponds to a maximum value selected between the pixel value of each pixel of the first difference frame and the pixel value of each corresponding pixel of the second difference frame; deriving a first mask frame by applying a normalization operation to the maximum value frame, wherein the normalization operation includes the step of dividing the pixel value of each pixel of the maximum value frame by the pixel value of the pixel in the maximum value frame having the highest pixel value in the maximum value frame; deriving a second mask frame by performing the following steps:raising each pixel value of the first mask frame to a power, and subtracting each raised pixel value from the value of 1; and deriving an enhanced frame by multiplying each pixel value of a selected one of the plurality of frames by a corresponding pixel value of the second mask frame; and displaying an image corresponding to the enhanced frame.
 76. The method according to claim 75, wherein:the bodily lumen is a blood vessel, each pixel corresponding to each frame of the plurality of frames represents one of a blood flow through the blood vessel, a vessel tissue of the blood vessel, and an exterior tissue surrounding the blood vessel, an appearance of the blood flow and an appearance of the exterior tissue in the image corresponding to the enhanced frame are suppressed, and an appearance of the vessel tissue is enhanced.
 77. A method for performing an automatic matching operation with respect to an IVUS imaging operation, comprising the steps of:placing within a bodily vessel an ultrasound signal transmitter and an ultrasound signal detector; moving each of the ultrasound signal transmitter and the ultrasound signal detector along a length of a first segment of the bodily vessel; detecting a first plurality of ultrasound signals reflected from at least a portion of the bodily vessel; processing and digitizing a first set of ultrasound signals obtained from the first plurality of reflected ultrasound signals in order to produce a first plurality of sets of digitized samples; deriving a first plurality of successive frames for a first pullback film from the first plurality of sets of digitized samples, wherein:each frame includes a first plurality of elements, each element represents one of the first plurality of digitized samples corresponding to one of the first plurality of reflected ultrasound signals from a first predetermined spatial location of the bodily vessel, each frame representing a complete cross section of the bodily vessel, and each frame is configured in at least one of polar coordinates and Cartesian coordinates; moving each of the ultrasound signal transmitter and the ultrasound signal detector along a length of a second segment of the bodily vessel, the second segment substantially overlapping the first segment; detecting a second plurality of ultrasound signals reflected from at least a portion of the bodily vessel; processing and digitizing a second set of ultrasound signals obtained from the second plurality of reflected ultrasound signals in order to produce a second plurality of sets of digitized samples; deriving a second plurality of successive frames for a second pullback film from the second plurality of sets of digitized samples, wherein:each frame of the second plurality of successive frames includes a second plurality of elements, each element of the second plurality of elements represents one of the second plurality of digitized samples corresponding to one of the second plurality of reflected ultrasound signals from a second predetermined spatial location of the bodily vessel, each frame of the second plurality of successive frames representing the complete cross section of the bodily vessel, and each frame of the second plurality of successive frames is configured in at least one of polar coordinates and Cartesian coordinates; selecting as a reference segment a subset of successive frames from the second pullback film, the reference segment representing a section of the bodily vessel also represented by a subset of successive frames derived from the first pullback film and formed as a second segment; and automatically matching between the reference segment and the second segment by performing closeness operations between frames of the first pullback film and frames of the reference segment.
 78. The method according to claim 77, wherein:the reference segment includes m successive frames, m being a positive integer, and the step of automatically matching includes the steps of:(a) selecting m successive frames from the first pullback film, (b) performing a closeness operation between each frame of the reference segment and each of the m frames selected in step (a) in order to produce m closeness values, (c) averaging the m closeness values onto a single value of a one dimensional closeness function, and (d) repeating steps (a) to (c), each repetition including a relative shift of the reference segment with respect to the first pullback film by at least one frame and each shift resulting in a selection of a new set of m successive frames in step (a), until a maximal value of the closeness function is indicative that a matching between the reference segment and the second segment has been accomplished.
 79. The method according to claim 78, further comprising the step of:prior to performing step (b), stabilizing each of the frames of the reference segment with respect to a counterpart frame of the selected m successive frames in order to compensate for at least one of a rigid rotational motion and a rigid Cartesian displacement.
 80. The method according to claim 77, further comprising the step of:displaying the first pullback film synchronously with the second pullback film so that an image of the first pullback film displayed simultaneously with an image of the second pullback film both correspond to the same location along the bodily vessel.
 81. The method according to claim 77, wherein after performing the step of moving each of the ultrasound signal transmitter and the ultrasound signal detector along the length of the first segment of the bodily vessel, the method comprises the steps of:removing from the bodily vessel the ultrasound signal transmitter and the ultrasound signal detector, and reinserting into the bodily vessel the ultrasound signal transmitter and the ultrasound signal detector in order to perform the step of moving each of the ultrasound signal transmitter and the ultrasound signal detector along the length of the second segment of the bodily vessel.
 82. A method for monitoring a local vasomotion of a blood vessel, comprising the steps of:placing within a blood vessel an ultrasound signal transmitter and an ultrasound signal detector; detecting a plurality of ultrasound signals reflected from at least a portion of the blood vessel; processing and digitizing a first set of ultrasound signals obtained from the plurality of reflected ultrasound signals in order to produce at least a first set of digitized samples and a second set of digitized samples; deriving from the first set of digitized samples a first two dimensional array including a set of data, wherein:the first two dimensional array includes a first plurality of elements, each of the first plurality of elements representing a digitized sample corresponding to one of the plurality of reflected ultrasound signals from a predetermined spatial location of the bodily vessel, the set of data of the first two dimensional array representing a complete cross section of the blood vessel, the first two dimensional array is configured in polar vectors, a first axis of the first two dimensional array represents an r coordinate, and a second axis of the first two dimensional array represents an angular coordinate; deriving from the second set of digitized samples a second two dimensional array including a set of data, wherein:the second two dimensional array includes a second plurality of elements, each of the second plurality of elements representing a digitized sample corresponding to one of the plurality of reflected ultrasound signals from the predetermined spatial location, the set of data of the second two dimensional array represents the complete cross section of the blood vessel, the second two dimensional array is configured in polar vectors, a first axis of the second two dimensional array represents the r coordinate, a second axis of the second two dimensional array axis represents the angular coordinate; and detecting a magnitude and a direction of the local vasomotion by applying a plurality of one dimensional shifts on the set of data of each polar vector of the second two dimensional array and by applying a closeness operation between the shifted set of data of each polar vector of the second two dimensional array and between the set of data of each corresponding polar vector of the first two dimensional array.
 83. The method according to claim 82, wherein the detection of the local vasomotion results in a local vasomotion value for each polar vector of the second two dimensional array, and wherein the method further comprises the step of averaging each local vasomotion value in order to produce a single average vasomotion value for the second two dimensional array, the single average vasomotion value indicating a global vasomotion.
 84. The method according to claim 83, further comprising the steps of:continuously producing additional two dimensional arrays; detecting and averaging additional local vasomotion values in each additional two dimensional array in order to produce an additional single average vasomotion value for each additional two dimensional array; and displaying each additional single average vasomotion value together as a vasomotion curve. 